bibliography.bib

@report{Xie2021,
   abstract = {Figure 1: Comparisons of different schemes for generating oriented proposals. (a) Rotated RPN densely places rotated anchors with different scales, ratios, and angles. (b) RoI Transformer + learns oriented proposal from horizontal RoI. It involvs RPN, RoI Alignment, and regression. (c) Our proposed oriented RPN generates high-quality proposals in a nearly cost-free manner. The number of parameters of oriented RPN is about 1/3000 of RoI Transformer + and 1/15 of rotated RPN. Abstract Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors' speed, thereby becoming the computational bottleneck in advanced oriented object detection systems. This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN, which is a general two-stage oriented detector with promising accuracy and efficiency. To be specific, in the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them. Without tricks, oriented R-CNN with ResNet50 achieves state-of-the-art detection accuracy on two commonly-used datasets for oriented object detection including DOTA (75.87% mAP) and HRSC2016 (96.50% mAP), while having a speed of 15.1 FPS with the image size of 1024×1024 on a single RTX 2080Ti. We hope our work could inspire rethinking the design of oriented detectors and serve as a baseline for oriented object detection. Code is available at https: //github.com/jbwang1997/OBBDetection.},
   author = {Xingxing Xie and Gong Cheng and Jiabao Wang and Xiwen Yao and Junwei Han},
   title = {Oriented R-CNN for Object Detection},
   url = {https://github.com/jbwang1997/},
   year = {2021},
}
@report{Ding2019,
   abstract = {Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and local-ization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light-head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.},
   author = {Jian Ding and Nan Xue and Yang Long and Gui-Song Xia and Qikai Lu},
   title = {Learning RoI Transformer for Oriented Object Detection in Aerial Images},
   year = {2019},
}
@report{Tanino2013,
   abstract = {Conclusion Radiation-induced microbleeds occurred more frequently in the present study than has been previously reported. PSI can be used to detect these vascu-lar changes earlier than other conventional MR imaging techniques.},
   author = {Tomohiko Tanino and Yoshiko Kanasaki and Takatoshi Tahara and Koichi Michimoto and Kazuhiko Kodani and Suguru Kakite and Toshio Kaminou and Takashi Watanabe and Toshihide Ogawa},
   journal = {Yonago Acta medica},
   keywords = {cranial irradiation,magnetic resonance imaging,radiation injury},
   pages = {7-12},
   title = {Radiation-Induced Microbleeds after Cranial Irradiation: Evaluation by Phase-Sensitive Magnetic Resonance Imaging with 3.0 Tesla},
   volume = {56},
   year = {2013},
}
@article{Nagata2012,
   abstract = {Background: Since the Nun Study in which cerebrovascular lesions (CVLs) were closely associated with the presence and severity of clinical symptoms in Alzheimer's disease (AD), vascular lesions have been drawing attention in understanding the pathophysiology underlying AD patients, especially elderly AD cases. The present study was conducted to elucidate the relationship between the CVLs and vascular risk factors (VRFs) in elderly AD patients. Methods: The present study was based on 120 cases (41 men and 79 women) who were diagnosed as having probable AD cases according to the NINCDS-ADRDA criteria. Their mean age was 75.6 years. All subjects underwent 1.5 Tesla MRI, neuropsychological evaluation and laboratory tests including brain natriuretic peptide (BNP) and ApoE. Hypertension diabetes mellitus (DM), dyslipidemia (DL), congestive heart failure (CHF), coronary artery disease (CAD), atrial fibrillation (AF) and hypotension were regarded as VRFs. The subjects were divided into two age groups: young-old group (YOG) consisting of 55 cases who were younger than 75 years and old-old group (OOG) consisting of 65 cases who were 75 years or older. Results: Seventeen cases (14.2%) had 1 VRF, 46 cases (38.3%) had 2 VRF2, 37 cases (30.8%) had 3 VRFs and 15 cases (12.5%) had 4 VRFs. 69 cases (57.5%) had APOE- 34. On MRI findings, only 10 cases (8.3%) showed brain atrophy without CVLs, 61 cases (50.8%) showed lacunar lesions, 46 cases (38.3%) showed leuko-araiosis in addition to the brain atrophy, and 3 cases (2.5%) were diagnosed as having a superficial siderosis. Forty-three cases (66.2%) showed lacunar lesions in OOG, whereas only 18 cases (32.7%) showed lacunar lesions in YOG. Thalamic lacunar lesions were detected in 18 cases (15.0%) and basal ganglionic lacunar lesions were seen in 64 cases (54.2%). Old microbleeds (OMBs) were detected in 13 of 51 (17.5%) cases with APOE- 34, whereas only 7 of 69 (10.1%) cases without APOE- 34 showed OMBs. More marked leuko-araiosis was observed in OOG and in YOG. Conclusions: As the severity of CVLs was associated with age number of VRF in our AD patients, VRFs may modify the pathophysiology and clinical presentation in elderly AD patients.},
   author = {Ken Nagata and Takashi Yamazaki and Daiki Takano and Tetsuya Maeda and Yasuko Ikeda and Yuichi Satoh and Taizen Nakase},
   doi = {10.1016/j.jalz.2012.05.1410},
   issn = {1552-5260},
   issue = {4S_Part_14},
   journal = {Alzheimer's & Dementia},
   month = {7},
   publisher = {Wiley},
   title = {P3‐190: Cerebrovascular lesions and vascular risk factors in patients with Alzheimer's disease},
   volume = {8},
   year = {2012},
}
@report{Huang2015,
   abstract = {Objective: To compare the frequency of microbleeds identified by susceptibility-weighted MRI (SWMRI) in patients with mild traumatic brain injury (mTBI) and normal controls, and correlate these findings with neuropsychological tests. Methods: Research ethics committee approval and patient written informed consents were obtained. One hundred eleven patients with mTBI without parenchymal hemorrhage on CT and conventional MRI received SWMRI as well as a digit span and continuous performance test. One hundred eleven healthy volunteers without history of traumatic brain injury were enrolled as the control group and received conventional MRI with additional SWMRI study. We analyzed the number and location of microbleeds in both groups. Results: Twenty-six patients with mTBI and 12 control subjects presented microbleeds on SWMRI (p 5 0.0197). Sixty microbleeds were found in 26 patients with mTBI and 15 microbleeds in 12 control subjects. The mTBI group showed notably more microbleeds in the cortex/subcortical region (52 microbleeds, 86.7%, vs 3 microbleeds, 20%; p , 0.0001). Conversely, the control group showed more microbleeds in the central brain (9 microbleeds, 60%, vs 3 microbleeds, 5%; p , 0.0001). There was no statistical difference in number of microbleeds in the cerebellum and brain-stem (p 5 0.2598 and p 5 0.4932, respectively). Patients with mTBI who had detected microbleeds had lower digit span scores than the patients with negative SWMRI findings (p 5 0.017). Conclusion: Presence of mTBI-related microbleeds showed a neuropsychological defect on short-term memory function, indicating that the presence of microbleeds could be a possible severity biomarker for mTBI. Addition of the SWMRI technique to the MRI protocol for patients with mTBI is recommended. Neurology ® 2015;84:580-585 GLOSSARY CPT 5 continuous performance test; mTBI 5 mild traumatic brain injury; SWAN 5 susceptibility-weighted angiography; SWMRI 5 susceptibility-weighted MRI; TBI 5 traumatic brain injury; TE 5 echo time; TR 5 repetition time. Traumatic brain injury (TBI) is a common neurologic condition, and more than 75% of TBIs are classified as mild traumatic brain injury (mTBI) by definition of the American Congress of Rehabilitative Medicine. 1-4 The course of mTBI is usually self-limited, but some patients experience dysfunctions that persist for life, leading to disability in social interaction and daily activities. 5,6 To date, there is a lack of effective clinical, laboratory, or imaging markers as prognostic factors for patients with mTBI. Pathophysiology of various symptoms in patients with mTBI still remains poorly understood. CT is the primary imaging examination of mTBI, but nearly always reveals negative findings. Susceptibility-weighted MRI (SWMRI) techniques are particularly helpful in detecting para-magnetic blood products. 7-10 The 2 most common SWMRI techniques in use are susceptibility-weighted imaging and susceptibility-weighted angiography (SWAN), both of which have similar ability in the detection of cerebral microbleeds and are superior to traditional T2*-weighted gradient-recall echo. 11 SWMRI has shown greater accuracy in detecting traumatic-related injuries, such as diffuse axonal and vascular injuries, than CT and conventional MRI techniques.},
   author = {Yen-Lin Huang and Ying-Sheng Kuo and Ying-Chi Tseng and David Yen-Ting Chen and Wen-Ta Chiu and Chi-Jen Chen},
   title = {Susceptibility-weighted MRI in mild traumatic brain injury From the Department of Diagnostic Radiology (Y},
   year = {2015},
}
@article{Beaman2022,
   abstract = {Background and Objectives Age-related cognitive impairment is driven by the complex interplay of neurovascular and neurodegenerative disease. There is a strong relationship between cerebral microbleeds (CMBs), cerebral amyloid angiopathy (CAA), and the cognitive decline observed in conditions such as Alzheimer disease. However, in the early, preclinical phase of cognitive impairment, the extent to which CMBs and underlying CAA affect volumetric changes in the brain related to neurodegenerative disease remains unclear. Methods We performed cross-sectional analyses from 3 large cohorts: The Northern Manhattan Study (NOMAS), Alzheimer's Disease Neuroimaging Initiative (ADNI), and the Epidemiology of Dementia in Singapore study (EDIS). We conducted a confirmatory analysis of 82 autopsied cases from the Brain Arterial Remodeling Study (BARS). We implemented multivariate regression analyses to study the association between 2 related markers of cerebrovascular disease-MRI-based CMBs and autopsy-based CAA-as independent variables and volumetric markers of neurodegeneration as dependent variables. NOMAS included mostly dementia-free participants age 55 years or older from northern Manhattan. ADNI included participants living in the United States age 55-90 years with a range of cognitive status. EDIS included community-based participants living in Singapore age 60 years and older with a range of cognitive status. BARS included postmortem pathologic samples. Results We included 2,657 participants with available MRI data and 82 autopsy cases from BARS. In a meta-analysis of NOMAS, ADNI, and EDIS, superficial CMBs were associated with larger gray matter (β = 4.49 ± 1.13, p = 0.04) and white matter (β = 4.72 ± 2.1, p = 0.03) volumes. The association between superficial CMBs and larger white matter volume was more evident in participants with 1 CMB (β = 5.17 ± 2.47, p = 0.04) than in those with ≥2 CMBs (β = 1.97 ± 3.41, p = 0.56). In BARS, CAA was associated with increased cortical thickness (β = 6.5 ± 2.3, p = 0.016) but not with increased brain weight (β = 1.54 ± 1.29, p = 0.26).},
   author = {Charles Beaman and Krystyna Kozii and Saima Hilal and Minghua Liu and Anthony J. Spagnolo-Allende and Guillermo Polanco-Serra and Christopher Chen and Ching Yu Cheng and Daniela Zambrano and Burak Arikan and Victor J. Del Brutto and Clinton Wright and Xena E. Flowers and Sandra P. Leskinen and Tatjana Rundek and Amanda Mitchell and Jean Paul Vonsattel and Etty Cortes and Andrew F. Teich and Ralph L. Sacco and Mitchell S.V. Elkind and David Roh and Jose Gutierrez},
   doi = {10.1212/WNL.0000000000200142},
   issn = {1526632X},
   issue = {16},
   journal = {Neurology},
   month = {4},
   pages = {E1605-E1616},
   pmid = {35228332},
   publisher = {Lippincott Williams and Wilkins},
   title = {Cerebral Microbleeds, Cerebral Amyloid Angiopathy, and Their Relationships to Quantitative Markers of Neurodegeneration},
   volume = {98},
   year = {2022},
}
@article{Bokura2011,
   abstract = {Background and Purpose- Cerebral microbleeds (MBs) are frequently detected in patients with stroke, especially those who experience intracerebral hemorrhage. However, the clinical significance of MBs in subjects without cerebrovascular disease is still unclear. We performed a prospective study to determine whether the presence of MBs provides useful prognostic information in healthy elderly individuals. Methods- We tracked 2102 subjects (mean age, 62.1 years) over a mean interval of 3.6 years after they voluntarily participated in the brain checkup system at the Shimane Institute of Health Science. An initial assessment was performed to document the presence of MBs and silent ischemic brain lesions and to map the location of the MBs. During the follow-up period, we obtained information about stroke events that occurred in each subject. Results- MBs were detected in 93 of the 2102 subjects (4.4%). Strokes occurred in 44 subjects (2.1%) during the follow-up period. They were significantly more common among subjects with MBs. Age and hypertension were independent risk factors for MBs. The presence of MBs was more strongly associated with a deep brain hemorrhage (hazard ratio, 50.2; 95% CI, 16.7 to 150.9) than ischemic stroke (hazard ratio, 4.48; 95% CI, 2.20 to 12.2). All hemorrhagic strokes occurred in deep brain regions, and they were associated with MBs located in the deep brain region. Conclusions- This longitudinal study demonstrated that the presence of MBs can be used to predict hemorrhagic and ischemic stroke, even in healthy elderly individuals. Copyright © 2011 American Heart Association. All rights reserved.},
   author = {Hirokazu Bokura and Reiko Saika and Takuya Yamaguchi and Atsushi Nagai and Hiroaki Oguro and Shotai Kobayashi and Shuhei Yamaguchi},
   doi = {10.1161/STROKEAHA.110.601922},
   issn = {00392499},
   issue = {7},
   journal = {Stroke},
   keywords = {hypertension,intracerebral hemorrhage,magnetic resonance imaging,microbleeds,prevention,risk factor},
   month = {7},
   pages = {1867-1871},
   pmid = {21597015},
   title = {Microbleeds are associated with subsequent hemorrhagic and ischemic stroke in healthy elderly individuals},
   volume = {42},
   year = {2011},
}
@report{Lin2013,
   abstract = {Brain microbleed is a marker of small vessel microhemorrhagic or microaneurysmal lesions, which may induce intracerebral hemorrhage (ICH). This study to prospectively evaluated the association between microbleeds, hematoma and perihematomal edema volume, and various clinical data, as well as patient outcome. Thirty-one patients with ICH and 31 healthy age-matched subjects were enrolled in our study. They were divided into two groups according to the presence or absence of microbleeds detected by MRI. Serial clinical and laboratory data were recorded. Modified Rankin Scale and Barthel Index were estimated three months after hemorrhage. The major location of microbleeds among patients with ICH was the basal ganglia. The volume of perihe-matomal edema was correlated with the initial hematoma volume on the first, fifth and seventh days after hemorrhage in patients with microbleeds. For patients without microbleeds, this correlation was also significant on the seventh day. Cerebral microbleeds in patients with ICH, especially in the basal ganglia region, represent micro-angiopathy, and are associated with leakage of blood and formation of perihemorrhage edema. Brain microbleeds found in patients with ICH warrant further investigation for evaluation of stroke risk.},
   author = {Wei-ming Lin and Tse-yen Yang and Hsu-huei Weng and Chih-feng Chen and Ming-hsueh Lee and Jen-tsung Yang and Shaner NG Yeun Jao and Yuan-hsiung Tsai},
   journal = {The Neuroradiology Journal},
   keywords = {brain microbleeds,intracerebral hemorrhage,perihematomal edema,stroke},
   pages = {184-190},
   title = {Brain Microbleeds: Distribution and Influence on Hematoma and Perihematomal Edema in Patients with Primary Intracerebral Hemorrhage},
   volume = {26},
   url = {www.centauro.it},
   year = {2013},
}
@article{Billot2023,
   abstract = {Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.},
   author = {Benjamin Billot and Douglas N. Greve and Oula Puonti and Axel Thielscher and Koen Van Leemput and Bruce Fischl and Adrian V. Dalca and Juan Eugenio Iglesias},
   doi = {10.1016/j.media.2023.102789},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {CNN,Contrast and resolution invariance,Domain randomisation,Segmentation},
   month = {5},
   pmid = {36857946},
   publisher = {Elsevier B.V.},
   title = {SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining},
   volume = {86},
   year = {2023},
}
@generic{Fisher2013,
   abstract = {Anticoagulation is highly effective in preventing stroke due to atrial fibrillation, but numerous studies have demonstrated low utilization of anticoagulation for these patients. Assessment of clinicians' attitudes on this topic indicate that fear of intracerebral hemorrhage (ICH), rather than appreciation of anticoagulation benefits, largely drives clinical decision-making for treatment with anticoagulation in atrial fibrillation. Risk stratification strategies have been used for anticoagulation benefits and hemorrhage risk, but ICH is not specifically addressed in the commonly used hemorrhage risk stratification systems. Cerebral microbleeds are cerebral microscopic hemorrhages demonstrable by brain MRI, indicative of prior microhemorrhages, and predictive of future risk of ICH. Prevalence of cerebral microbleeds increases with age; and cross-sectional and limited prospective studies generally indicate that microbleeds confer substantial risk of ICH in patients treated with chronic anticoagulation. MRI thus is a readily available and appealing modality that can directly assess risk of future ICH in patients receiving anticoagulants for atrial fibrillation. Incorporation of MRI into routine practice is, however, fraught with difficulties, including the uncertain relationship between number and location of microbleeds and ICH risk, as well as cost-effectiveness of MRI. A proposed algorithm is provided, and relevant advantages and disadvantages are discussed. At present, MRI screening appears most appropriate for a subset of atrial fibrillation patients, such as those with intermediate stroke risk, and may provide reassurance for clinicians whose concerns for ICH tend to outweigh benefits of anticoagulation. © 2013 Fisher.},
   author = {Mark Fisher},
   doi = {10.3389/fneur.2013.00137},
   issn = {16642295},
   journal = {Frontiers in Neurology},
   keywords = {Anticoagulation,Atrial fibrillation,Hemorrhage,MRI,Microbleeds,Stroke},
   title = {MRI screening for chronic anticoagulation in atrial fibrillation},
   volume = {4 OCT},
   year = {2013},
}
@article{Fang2023,
   abstract = {Cerebral microbleed (CMB) based on magnetic resonance imaging has been recently investigated as key biomarker in the diagnosis of cerebral small-vessel diseases and vascular cognitive impairment. Because the CMB lesions are typically small in size, and easily confused with various analogs such as calcified deposits, artifacts, and especially blood vessels when they are observed from a single MRI slice, reducing false positives in CMB detection is quite challenging. In addition, the lack of available medical image data, which inevitably leads to the imbalance between positive and negative samples, is also a challenge to existing deep learning algorithms. To address these problems, this paper proposes a simple but effective CMB detection method based on a novel deep architecture. First, in contrast to the current local patches-based approach, we make full use of the information about the distribution of CMBs in the whole brain based on training data as priori knowledge to guide the model to obtain candidate CMB patches. Second, we propose a 2.5D convolutional neural network based on morphological differences in cerebral blood vessels and cerebral microbleeds. Specifically, we further use information of the candidates in the coronal and sagittal planes and combine the inference based on three planes to determine the CMB probability of each patch. This approach strikes a balance between the high computational cost and the loss of spatial information. The effectiveness of the proposed method is demonstrated through experimental results that show that our KBPNet model has a sensitivity of 98.24%, an accuracy of 94.10% and an average number of false positives per patient of 1.72 on the SWI-CMB dataset.},
   author = {Zhongding Fang and Rong Zhang and Lijun Guo and Tianxiang Xia and Yingqing Zeng and Xiping Wu},
   doi = {10.1016/j.bspc.2023.105078},
   issn = {17468108},
   journal = {Biomedical Signal Processing and Control},
   keywords = {Body plane,Cerebral microbleeds,Deep learning,Priori knowledge},
   month = {9},
   publisher = {Elsevier Ltd},
   title = {Knowledge-guided 2.5D CNN for cerebral microbleeds detection},
   volume = {86},
   year = {2023},
}
@article{Sundaresan2023,
   abstract = {Introduction: Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods: In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results: On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.},
   author = {Vaanathi Sundaresan and Christoph Arthofer and Giovanna Zamboni and Andrew G. Murchison and Robert A. Dineen and Peter M. Rothwell and Dorothee P. Auer and Chaoyue Wang and Karla L. Miller and Benjamin C. Tendler and Fidel Alfaro-Almagro and Stamatios N. Sotiropoulos and Nikola Sprigg and Ludovica Griffanti and Mark Jenkinson},
   doi = {10.3389/fninf.2023.1204186},
   issn = {16625196},
   journal = {Frontiers in Neuroinformatics},
   keywords = {cerebral microbleed (CMB),deep learning,detection,knowledge distillation,magnetic resonance imaging,quantitative susceptibility mapping (QSM),susceptibility-weighted image (SWI)},
   publisher = {Frontiers Media SA},
   title = {Automated detection of cerebral microbleeds on MR images using knowledge distillation framework},
   volume = {17},
   year = {2023},
}
@article{Ferrer2023,
   abstract = {Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging. Recent studies on COVID-19 have shown an increase in CMBs of coronavirus cases. Automatic detection of CMBs is challenging due to the small size and amount of CMBs making the classes highly imbalanced, lack of publicly available annotated data, and similarity with CMB mimics such as calcifications, irons, and veins. Hence, the existing deep learning methods are mostly trained on very limited research data and fail to generalize to unseen data with high variability and cannot be used in clinical setups. To this end, we propose an efficient 3D deep learning framework that is actively trained on multi-domain data. Two public datasets assigned for normal aging, stroke, and Alzheimer's disease analysis as well as an in-house dataset for COVID-19 assessment are used to train and evaluate the models. The obtained results show that the proposed method is robust to low-resolution images and achieves 78% recall and 80% precision on the entire test set with an average false positive of 1.6 per scan.},
   author = {Neus Rodeja Ferrer and Malini Vendela Sagar and Kiril Vadimovic Klein and Christina Kruuse and Mads Nielsen and Mostafa Mehdipour Ghazi},
   month = {1},
   title = {Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19},
   url = {http://arxiv.org/abs/2301.09322},
   year = {2023},
}
@article{Wu2023,
   abstract = {Background: Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score. Results: A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895–0.921), accuracy of 0.819 (95% CI 0.768–0.870), weighted-average precision of 0.864 (95% CI 0.831–0.897), and weighted-average F1-score of 0.829 (95% CI 0.782–0.876) in the external set, outperforming the performance of the neurologist group. Conclusion: The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.},
   author = {Ruizhen Wu and Huaqing Liu and Hao Li and Lifen Chen and Lei Wei and Xuehong Huang and Xu Liu and Xuejiao Men and Xidan Li and Lanqing Han and Zhengqi Lu and Bing Qin},
   doi = {10.1186/s12938-023-01164-1},
   issn = {1475925X},
   issue = {1},
   journal = {BioMedical Engineering Online},
   keywords = {Cerebral microbleeds,Cerebral small vessel disease,Deep learning,Susceptibility-weighted MR Sequence},
   month = {12},
   pmid = {37848906},
   publisher = {BioMed Central Ltd},
   title = {Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease},
   volume = {22},
   year = {2023},
}
@article{Ali2023,
   author = {Zeeshan Ali and Sheneela Naz and Sadaf Yasmin and Maryam Bukhari and Mucheol Kim},
   doi = {10.1016/j.heliyon.2023.e22879},
   issn = {24058440},
   issue = {12},
   journal = {Heliyon},
   month = {12},
   pages = {e22879},
   title = {Deep learning-assisted IoMT framework for cerebral microbleed detection},
   volume = {9},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S2405844023100879},
   year = {2023},
}
@generic{ZhaoX2021,
   abstract = {Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep learning has shown superior performance over traditional machine learning approaches in image analysis. In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.},
   author = {Xingzhong Zhao and Xing Ming Zhao},
   doi = {10.1016/j.ymeth.2020.09.007},
   issn = {10959130},
   journal = {Methods},
   keywords = {Brain MR Image,Computer-aided diagnosis,Deep learning,Image preprocessing},
   month = {8},
   pages = {131-140},
   pmid = {32931932},
   publisher = {Academic Press Inc.},
   title = {Deep learning of brain magnetic resonance images: A brief review},
   volume = {192},
   year = {2021},
}
@article{Zinnel2023,
   abstract = {The detection and classification of traumatic brain injury (TBI) by medical professionals can vary due to subjectivity and differences in experience. Thus, a computational approach for detecting and classifying TBI would be invaluable for an objective diagnosis of this injury. In this review paper, various machine learning algorithms used to detect, classify, and predict the severity and outcomes of TBI in a clinical setting are discussed. The most promising of these algorithms is the convolutional neural network (CNN), which is highlighted in the review.},
   author = {Laura Zinnel and Sarah A. Bentil},
   doi = {10.1016/j.hsr.2023.100126},
   issn = {27726320},
   journal = {Health Sciences Review},
   month = {12},
   pages = {100126},
   publisher = {Elsevier BV},
   title = {Convolutional neural networks for traumatic brain injury classification and outcome prediction},
   volume = {9},
   year = {2023},
}
@generic{Humphries2019,
   abstract = {Objective: The term cerebral microbleed (CMB) refers to lesions documented as unexpected findings during computed tomography or magnetic resonance imaging examination of the brain. Initially, a CMB was thought to represent hemosiderin-laden macrophages marking an area of a tiny hemorrhage. Recently, histopathologic studies have shown that the structure of a CMB can be variable. To aid in dealing with this finding and judging its clinical significance, this review addresses important aspects of a CMB, including the definition, prevalence, and incidence in various populations, end-organ damage, associated conditions, and whether any action or treatment by the clinician might be indicated. Methods: PubMed Medline, EMBASE, BIOSIS, Current Contents, and Derwent Drug Files databases were searched for the keywords “microbleeds-detection-damage”, “silent bleeds”, “microbleeds”, or “silent bleeds AND hemophilia” from 2011–2016. References of retrieved articles were also reviewed and included if applicable. Results: The published data are found primarily in the imaging literature and focus on diagnostic techniques. Some publications address relationships with diverse, co-existing clinical conditions and implications for treatment, especially in stroke, intracranial hemorrhage, and antithrombotic therapy. Conclusions: It is critical for non-radiologist clinicians (primary care, internists, neurologists, hematologists) to be aware of the potential importance of the finding of a CMB, and the fact that these lesions are not always truly silent or without important clinical consequences. As additional studies appear, clinicians may be able to “hear” more clearly through the silence of the CMB and understand potential clinical implications in patients.},
   author = {Thomas J. Humphries and Prasad Mathew},
   doi = {10.1080/03007995.2018.1521787},
   issn = {14734877},
   issue = {2},
   journal = {Current Medical Research and Opinion},
   keywords = {General practice,Internal medicine,Magnetic resonance imaging,Risk factors},
   month = {2},
   pages = {359-366},
   pmid = {30193542},
   publisher = {Taylor and Francis Ltd},
   title = {Cerebral microbleeds: hearing through the silence—a narrative review},
   volume = {35},
   year = {2019},
}
@article{Alberts2023,
   author = {Amelia Alberts and Brandon Lucke-Wold},
   doi = {10.31083/j.jin2206142},
   issn = {0219-6352},
   issue = {6},
   journal = {Journal of Integrative Neuroscience},
   month = {10},
   pages = {142},
   publisher = {IMR Press},
   title = {Updates on Improving Imaging Modalities for Traumatic Brain Injury},
   volume = {22},
   year = {2023},
}
@generic{Zhao2021,
   abstract = {The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.},
   author = {Lei Zhao and Allan Lee and Yu Hua Fan and Vincent C.T. Mok and Lin Shi},
   doi = {10.1097/CM9.0000000000001299},
   issn = {25425641},
   issue = {2},
   journal = {Chinese Medical Journal},
   keywords = {Automated quantification,Cerebral small vessel disease,Clinical relevance,Neuroimaging manifestations},
   month = {1},
   pages = {151-160},
   pmid = {33443936},
   publisher = {Lippincott Williams and Wilkins},
   title = {Magnetic resonance imaging manifestations of cerebral small vessel disease: Automated quantification and clinical application},
   volume = {134},
   year = {2021},
}
@article{Abraham2018,
   abstract = {We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.},
   author = {Nabila Abraham and Naimul Mefraz Khan},
   month = {10},
   title = {A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation},
   url = {http://arxiv.org/abs/1810.07842},
   year = {2018},
}
@article{Horien2021,
   abstract = {Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.},
   author = {Corey Horien and Stephanie Noble and Abigail S. Greene and Kangjoo Lee and Daniel S. Barron and Siyuan Gao and David O’Connor and Mehraveh Salehi and Javid Dadashkarimi and Xilin Shen and Evelyn M.R. Lake and R. Todd Constable and Dustin Scheinost},
   doi = {10.1038/s41562-020-01005-4},
   issn = {23973374},
   issue = {2},
   journal = {Nature Human Behaviour},
   month = {2},
   pages = {185-193},
   pmid = {33288916},
   publisher = {Nature Research},
   title = {A hitchhiker’s guide to working with large, open-source neuroimaging datasets},
   volume = {5},
   year = {2021},
}
@article{Buch2017,
   abstract = {Cerebral microbleeds (CMBs) are small brain hemorrhages caused by the break down or structural abnormalities of small vessels of the brain. Owing to the paramagnetic properties of blood degradation products, CMBs can be detected in vivo using susceptibility-weighted imaging (SWI). SWI can be used not only to detect iron changes and CMBs, but also to differentiate them from calcifications, both of which may be important MR-based biomarkers for neurodegenerative diseases. Moreover, SWI can be used to quantify the iron in CMBs. SWI and gradient echo (GE) imaging are the two most common methods for the detection of iron deposition and CMBs. This study provides a comprehensive analysis of the number of voxels detected in the presence of a CMB on GE magnitude, phase and SWI composite images as a function of resolution, signal-to-noise ratio (SNR), TE, field strength and susceptibility using in silico experiments. Susceptibility maps were used to quantify the bias in the effective susceptibility value and to determine the optimal TE for CMB quantification. We observed a non-linear trend with susceptibility for CMB detection from the magnitude images, but a linear trend with susceptibility for CMB detection from the phase and SWI composite images. The optimal TE values for CMB quantification were found to be 3 ms at 7 T, 7 ms at 3 T and 14 ms at 1.5 T for a CMB of one voxel in diameter with an SNR of 20: 1. The simulations of signal loss and detectability were used to generate theoretical formulae for predictions. Copyright © 2016 John Wiley & Sons, Ltd.},
   author = {Sagar Buch and Yu Chung N. Cheng and Jiani Hu and Saifeng Liu and John Beaver and Rajasimhan Rajagovindan and E. Mark Haacke},
   doi = {10.1002/nbm.3551},
   issn = {10991492},
   issue = {4},
   journal = {NMR in Biomedicine},
   keywords = {quantification,relaxometry,susceptibility-weighted imaging,visualization},
   month = {4},
   pmid = {27206271},
   publisher = {John Wiley and Sons Ltd},
   title = {Determination of detection sensitivity for cerebral microbleeds using susceptibility-weighted imaging},
   volume = {30},
   year = {2017},
}
@article{Cheng2021,
   abstract = {We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects. The new quality measure displays several desirable characteristics like symmetry w.r.t. prediction/ground truth pairs and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other boundary-focused measures like Trimap IoU and F-measure. Based on Boundary IoU, we update the standard evaluation protocols for instance and panoptic segmentation tasks by proposing the Boundary AP (Average Precision) and Boundary PQ (Panoptic Quality) metrics, respectively. Our experiments show that the new evaluation metrics track boundary quality improvements that are generally overlooked by current Mask IoU-based evaluation metrics. We hope that the adoption of the new boundary-sensitive evaluation metrics will lead to rapid progress in segmentation methods that improve boundary quality.},
   author = {Bowen Cheng and Ross Girshick and Piotr Dollár and Alexander C. Berg and Alexander Kirillov},
   month = {3},
   title = {Boundary IoU: Improving Object-Centric Image Segmentation Evaluation},
   url = {http://arxiv.org/abs/2103.16562},
   year = {2021},
}
@generic{Shah2019,
   author = {Nigam H. Shah and Arnold Milstein and Steven C. Bagley},
   doi = {10.1001/jama.2019.10306},
   issn = {15383598},
   issue = {14},
   journal = {JAMA - Journal of the American Medical Association},
   month = {10},
   pages = {1351-1352},
   pmid = {31393527},
   publisher = {American Medical Association},
   title = {Making Machine Learning Models Clinically Useful},
   volume = {322},
   year = {2019},
}
@generic{Kelly2019,
   abstract = {Background: Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Main body: Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. Conclusion: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.},
   author = {Christopher J. Kelly and Alan Karthikesalingam and Mustafa Suleyman and Greg Corrado and Dominic King},
   doi = {10.1186/s12916-019-1426-2},
   issn = {17417015},
   issue = {1},
   journal = {BMC Medicine},
   keywords = {Algorithms,Artificial intelligence,Evaluation,Machine learning,Regulation,Translation},
   month = {10},
   pmid = {31665002},
   publisher = {BioMed Central Ltd.},
   title = {Key challenges for delivering clinical impact with artificial intelligence},
   volume = {17},
   year = {2019},
}
@article{Xie2023,
   abstract = {Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.},
   author = {Yutong Xie and Bing Yang and Qingbiao Guan and Jianpeng Zhang and Qi Wu and Yong Xia},
   month = {5},
   title = {Attention Mechanisms in Medical Image Segmentation: A Survey},
   url = {http://arxiv.org/abs/2305.17937},
   year = {2023},
}
@article{Ronneberger2015,
   abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
   author = {Olaf Ronneberger and Philipp Fischer and Thomas Brox},
   month = {5},
   title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
   url = {http://arxiv.org/abs/1505.04597},
   year = {2015},
}
@inproceedings{Agarwal2022,
   author = {Aman Agarwal and Rakesh Kumar and Meenu Gupta},
   isbn = {9781665454155},
   publisher = {IEEE},
   title = {Review on Deep Learning based Medical Image Processing},
   year = {2022},
}
@generic{Litjens2017,
   abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.},
   author = {Geert Litjens and Thijs Kooi and Babak Ehteshami Bejnordi and Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Mohsen Ghafoorian and Jeroen A.W.M. van der Laak and Bram van Ginneken and Clara I. Sánchez},
   doi = {10.1016/j.media.2017.07.005},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {Convolutional neural networks,Deep learning,Medical imaging,Survey},
   month = {12},
   pages = {60-88},
   pmid = {28778026},
   publisher = {Elsevier B.V.},
   title = {A survey on deep learning in medical image analysis},
   volume = {42},
   year = {2017},
}
@article{Qureshi2023,
   abstract = {Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors present perspectives on the development of an architectural, and operational mechanism of each machine learning-based semantic segmentation approach with merits and demerits. In this regard, researchers have proposed different Semseg methods and examined their performance in a variety of applications such as medical image analysis (e.g., medical image classification and segmentation). A review of recent advances in Semseg techniques are presented in this paper by applying computational image processing and machine learning methods. This article is further presented a comprehensive investigation on how different architectures are helpful for medical image segmentation. Finally, advantages, open challenges, and possible future directions are elaborated in the discussion part, beneficial to the research community to understand the significance of the available medical imaging segmentation technology based on Semseg and thus deliver robust segmentation solutions.},
   author = {Imran Qureshi and Junhua Yan and Qaisar Abbas and Kashif Shaheed and Awais Bin Riaz and Abdul Wahid and Muhammad Waseem Jan Khan and Piotr Szczuko},
   doi = {10.1016/j.inffus.2022.09.031},
   issn = {15662535},
   journal = {Information Fusion},
   keywords = {Deep learning,Medical imaging,Optimization techniques,Semantic segmentation,Transfer learning},
   month = {2},
   pages = {316-352},
   publisher = {Elsevier B.V.},
   title = {Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends},
   volume = {90},
   year = {2023},
}
@article{Shen2017,
   abstract = {This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.},
   author = {Dinggang Shen and Guorong Wu and Heung Il Suk},
   doi = {10.1146/annurev-bioeng-071516-044442},
   issn = {15454274},
   journal = {Annual Review of Biomedical Engineering},
   keywords = {Deep learning,Medical image analysis,Unsupervised feature learning},
   month = {6},
   pages = {221-248},
   pmid = {28301734},
   publisher = {Annual Reviews Inc.},
   title = {Deep Learning in Medical Image Analysis},
   volume = {19},
   year = {2017},
}
@generic{Lecun2015,
   abstract = {Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.},
   author = {Yann Lecun and Yoshua Bengio and Geoffrey Hinton},
   doi = {10.1038/nature14539},
   issn = {14764687},
   issue = {7553},
   journal = {Nature},
   month = {5},
   pages = {436-444},
   pmid = {26017442},
   publisher = {Nature Publishing Group},
   title = {Deep learning},
   volume = {521},
   year = {2015},
}
@generic{Sakshi2023,
   abstract = {Segmentation has been a rooted area of research having diverse dimensions. The roots of image segmentation and its associated techniques have supported computer vision, pattern recognition, image processing, and it holds variegated applications in crucial domains. To compile the vast literature on machine learning and deep learning-based segmentation techniques and proffer statistical, comprehensive, semi-automated, and application-specific analysis, which could contribute to the ongoing research. 16,674 studies have been filtered out from the pool of 22,088 studies collocated by executing a search string on the Scopus database. These studies are analyzed for their meta-data, comprehensive content and reviewed to identify key research areas using the topic modeling-based method (LDA). Also, the segmentation role for mathematical expression recognition has been fathomed out. IEEE is a ubiquitous name in the terms of the renowned publisher, reputed journal (IEEE Access), and most cited affiliation (#10,472). Three out of five extracted topic solutions by the LDA model be evidence of streaming research areas in image segmentation. Medical Image Processing, Machine Vision and Object Identification are the accentuated domains in the context. The streamlining of comprehensive analysis puts forth neural network-based approaches as a trend. Inquisition of segmentation techniques for mathematical expressions articulate neural-based segmentation techniques (CNN, RNN, LSTM) as preeminent segmentation techniques and geometrical features as focused features of the process. To sum up, the purpose of the current study is to summarize the best available research on image segmentation after synthesizing the results of an assorted set of studies.},
   author = {Sakshi and Vinay Kukreja},
   doi = {10.1007/s11831-022-09805-9},
   issn = {18861784},
   issue = {1},
   journal = {Archives of Computational Methods in Engineering},
   month = {1},
   pages = {457-495},
   publisher = {Springer Science and Business Media B.V.},
   title = {Image Segmentation Techniques: Statistical, Comprehensive, Semi-Automated Analysis and an Application Perspective Analysis of Mathematical Expressions},
   volume = {30},
   year = {2023},
}
@article{Ramesh2021,
   abstract = {INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM. OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation. METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM). CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.},
   author = {K. K.D. Ramesh and G. Kiran Kumar and K. Swapna and Debabrata Datta and S. Suman Rajest},
   doi = {10.4108/eai.12-4-2021.169184},
   issn = {24117145},
   issue = {27},
   journal = {EAI Endorsed Transactions on Pervasive Health and Technology},
   keywords = {Clustering,Computed tomography (CT),Edge detection,Image analysis,Image processing,Lattice Boltzmann method (LBM),Magnetic resonance imaging (MRI),Medical physics,Radiation therapy,Radiotherapy treatment planning systems (RTPS),Segmentation,Thresholding},
   publisher = {European Alliance for Innovation},
   title = {A review of medical image segmentation algorithms},
   volume = {7},
   year = {2021},
}
@article{Cordonnier2009,
   abstract = {BACKGROUND AND PURPOSE: If the diagnostic and prognostic significance of brain microbleeds (BMBs) are to be investigated and used for these purposes in clinical practice, observer variation in BMB assessment must be minimized. METHODS: Two doctors used a pilot rating scale to describe the number and distribution of BMBs (round, low-signal lesions, <10 mm diameter on gradient echo MRI) among 264 adults with stroke or TIA. They were blinded to clinical data and their counterpart's ratings. Disagreements were adjudicated by a third observer, who informed the development of a new Brain Observer MicroBleed Scale (BOMBS), which was tested in a separate cohort of 156 adults with stroke. RESULTS: In the pilot study, agreement about the presence of ĝ‰¥1 BMB in any location was moderate (κ0.44; 95% CI, 0.32-0.56), but agreement was worse in lobar locations (κ0.44; 95% CI, 0.30-0.58) than in deep (κ0.62; 95% CI, 0.48-0.76) or posterior fossa locations (κ0.66; 95% CI, 0.47-0.84). Using BOMBS, agreement about the presence of ĝ‰¥1 BMB improved in any location (κ0.68; 95% CI, 0.49-0.86) and in lobar locations (κ0.78; 95% CI, 0.60-0.97). CONCLUSION: Interrater reliability concerning the presence of BMBs was moderate to good, and could be improved with the use of the BOMBS rating scale, which takes into account the main sources of interrater disagreement identified by our pilot scale. Copyright © 2009 American Heart Association. All rights reserved.},
   author = {Charlotte Cordonnier and Gillian M. Potter and Caroline A. Jackson and Fergus Doubal and Sarah Keir and Cathie L.M. Sudlow and Joanna M. Wardlaw and Rustam Al-Shahi Salman},
   doi = {10.1161/STROKEAHA.108.526996},
   issn = {00392499},
   issue = {1},
   journal = {Stroke},
   keywords = {Brain microbleed,Classification,Interrater reliability,Rating scale,Stroke},
   month = {1},
   pages = {94-99},
   pmid = {19008468},
   title = {Improving interrater agreement about brain microbleeds: Development of the Brain Observer MicroBleed Scale (BOMBS)},
   volume = {40},
   year = {2009},
}
@article{Gregoire2009,
   abstract = {OBJECTIVE: Brain microbleeds on gradient-recalled echo (GRE) T2*-weighted MRI may be a useful biomarker for bleeding-prone small vessel diseases, with potential relevance for diagnosis, prognosis (especially for antithrombotic-related bleeding risk), and understanding mechanisms of symptoms, including cognitive impairment. To address these questions, it is necessary to reliably measure their presence and distribution in the brain. We designed and systematically validated the Microbleed Anatomical Rating Scale (MARS). We measured intrarater and interrater agreement for presence, number, and anatomical distribution of microbleeds using MARS across different MRI sequences and levels of observer experience. METHODS: We studied a population of 301 unselected consecutive patients admitted to our stroke unit using 2 GRE T2*-weighted MRI sequences (echo time [TE] 40 and 26 ms). Two independent raters with different MRI rating expertise identified, counted, and anatomically categorized microbleeds. RESULTS: At TE = 40 ms, agreement for microbleed presence in any brain location was good to very good (intrarater κ = 0.85 [95% confidence interval (CI) 0.77-0.93]; interrater κ = 0.68 [95% CI 0.58-0.78]). Good to very good agreement was reached for the presence of microbleeds in each anatomical region and in individual cerebral lobes. Intrarater and interrater reliability for the number of microbleeds was excellent (intraclass correlation coefficient [ICC] = 0.98 [95% CI 0.97-0.99] and ICC = 0.93 [0.91-0.94]). Very good interrater reliability was obtained at TE = 26 ms (κ = 0.87 [95% CI 0.61-1]) for definite microbleeds in any location. CONCLUSION: The Microbleed Anatomical Rating Scale has good intrarater and interrater reliability for the presence of definite microbleeds in all brain locations when applied to different MRI sequences and levels of observer experience. © 2009 by AAN Enterprises, Inc. All rights reserved.},
   author = {S. M. Gregoire and U. J. Chaudhary and M. M. Brown and T. A. Yousry and C. Kallis and H. R. Jäger and D. J. Werring},
   doi = {10.1212/WNL.0b013e3181c34a7d},
   issn = {1526632X},
   issue = {21},
   journal = {Neurology},
   pages = {1759-1766},
   pmid = {19933977},
   publisher = {Lippincott Williams and Wilkins},
   title = {The Microbleed Anatomical Rating Scale (MARS): Reliability of a tool to map brain microbleeds},
   volume = {73},
   year = {2009},
}
@generic{Mittal2009,
   abstract = {Susceptibility-weighted imaging (SWI) has continued to develop into a powerful clinical tool to visualize venous structures and iron in the brain and to study diverse pathologic conditions. SWI offers a unique contrast, different from spin attenuation, T1, T2, and T2* (see Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1). In this clinical review (Part 2), we present a variety of neurovascular and neurodegenerative disease applications for SWI, covering trauma, stroke, cerebral amyloid angiopathy, venous anomalies, multiple sclerosis, and tumors. We conclude that SWI often offers complementary information valuable in the diagnosis and potential treatment of patients with neurologic disorders.},
   author = {S. Mittal and Z. Wu and J. Neelavalli and E. M. Haacke},
   doi = {10.3174/ajnr.A1461},
   issn = {01956108},
   issue = {2},
   journal = {American Journal of Neuroradiology},
   month = {2},
   pages = {232-252},
   pmid = {19131406},
   title = {Susceptibility-weighted imaging: Technical aspects and clinical applications, part 2},
   volume = {30},
   year = {2009},
}
@article{Ibrahim2019,
   abstract = {Background: Cerebral microbleeds are small, round dark-signal foci in the T2*-weighted magnetic resonance imaging. They are encountered in cerebral amyloid angiopathy and hypertensive vasculopathy. Their prevalence is common in ischemic stroke and cerebral hemorrhage. The purpose of this study is to investigate the prevalence of CMBs and associated risk factors in the elderly patients with acute ischemic stroke. Results: Cerebral microbleeds were significantly associated with the presence of hypertension (in the subgroup of recurrent stroke) and with hypercholesterolemia. There was a significant association between the number of the microbleeds and severity of white matter lesions as a higher number of microbleeds related to more severe white matter lesions. The microbleeds were more prevalent in the group of patients using antithrombotics. Conclusion: Age, hypercholesterolemia, and the use of antithrombotics emerged as the most important associated risk factors for the presence of CMBs. On MRI, there was a significant association between the number of CMBs and severity of white matter lesions as a higher number of CMBs related to more severe white matter lesions.},
   author = {Abeer Abdelzaher Ibrahim and Yosra Abdelzaher Ibrahim and Eman A. Darwish and Nivan Hany Khater},
   doi = {10.1186/s43055-019-0034-7},
   issn = {20904762},
   issue = {1},
   journal = {Egyptian Journal of Radiology and Nuclear Medicine},
   keywords = {Cerebral microbleeds,Elderly,Ischemic stroke,T2*WI},
   month = {12},
   publisher = {Springer},
   title = {Prevalence of cerebral microbleeds and other cardiovascular risk factors in elderly patients with acute ischemic stroke},
   volume = {50},
   year = {2019},
}
@inproceedings{Poels2010,
   abstract = {BACKGROUND AND PURPOSE-: We previously reported on the high prevalence of cerebral microbleeds (CMBs) in community-dwelling people aged 60 years and older. Moreover, we found that their spatial distribution likely reflects differences in underlying etiology. We have since almost quadrupled the number of participants in our study and expanded it to include persons of 45 years and older. We examined the prevalence and determinants of microbleeds in this larger and younger cohort from the general population. METHODS-: In 3979 persons (mean age, 60.3 years), we performed brain MRI at 1.5T, including a sequence optimized for visualization of CMBs. Associations between APOE genotype, cardiovascular risk factors, and markers of cerebrovascular disease with the presence and location of CMBs were assessed by multiple logistic regression adjusted for age, sex, and relevant confounders. RESULTS-: Microbleed prevalence gradually increased with age, from 6.5% in persons aged 45 to 50 years to 35.7% in participants of 80 years and older. Overall, 15.3% of all subjects had at least 1 CMB. Cardiovascular risk factors and presence of lacunar infarcts and white matter lesions were associated with microbleeds in a deep or infratentorial region, whereas APOE &epsiv;4 and diastolic blood pressure were related to microbleeds in a strictly lobar location. CONCLUSIONS-: Findings in this larger population are in line with our previous results and, more importantly, extend these to a younger age group. CMBs are already present at middle age, and prevalence rises strongly with increasing age. We confirmed that determinants of the presence of cerebral microbleeds differ according to their location in the brain. © 2010 American Heart Association, Inc.},
   author = {Mariëlle M.F. Poels and Meike W. Vernooij and M. Arfan Ikram and Albert Hofman and Gabriel P. Krestin and Aad Van Der Lugt and Monique Breteler},
   doi = {10.1161/STROKEAHA.110.595181},
   issn = {00392499},
   issue = {10 SUPPL. 1},
   journal = {Stroke},
   keywords = {gradient echo MRI,intracerebral hemorrhage,microbleeds},
   month = {10},
   pmid = {20876479},
   title = {Prevalence and risk factors of cerebral microbleeds: An update of the rotterdam scan study},
   volume = {41},
   year = {2010},
}
@article{Shams2015,
   abstract = {BACKGROUND AND PURPOSE: Cerebral microbleeds are thought to have potentially important clinical implications in dementia and stroke. However, the use of both T2∗and SWI MR imaging sequences for microbleed detection has complicated the cross-comparison of study results. We aimed to determine the impact of microbleed sequences on microbleed detection and associated clinical parameters. MATERIALS AND METHODS: Patients from our memory clinic (n = 246; 53% female; mean age, 62) prospectively underwent 3T MR imaging, with conventional thick-section T2∗, thick-section SWI, and conventional thin-section SWI. Microbleeds were assessed separately on thick-section SWI, thin-section SWI, and T2∗by 3 raters, with varying neuroradiologic experience. Clinical and radiologic parameters from the dementia investigation were analyzed in association with the number of microbleeds in negative binomial regression analyses. RESULTS: Prevalence and number of microbleeds were higher on thick-/thin-section SWI (20/21%) compared with T2∗(17%). There was no difference in microbleed prevalence/number between thick- and thin-section SWI. Interrater agreement was excellent for all raters and sequences. Univariate comparisons of clinical parameters between patients with and without microbleeds yielded no difference across sequences. In the regression analysis, only minor differences in clinical associations with the number of microbleeds were noted across sequences. CONCLUSIONS: Due to the increased detection of microbleeds, we recommend SWI as the sequence of choice in microbleed detection. Microbleeds and their association with clinical parameters are robust to the effects of varying MR imaging sequences, suggesting that comparison of results across studies is possible, despite differing microbleed sequences.},
   author = {Sara Shams and J. Martola and L. Cavallin and T. Granberg and M. Shams and P. Aspelin and L. O. Wahlund and M. Kristoffersen-Wiberg},
   doi = {10.3174/ajnr.A4248},
   issn = {1936959X},
   issue = {6},
   journal = {American Journal of Neuroradiology},
   month = {6},
   pages = {1089-1095},
   pmid = {25698623},
   publisher = {American Society of Neuroradiology},
   title = {SWI or T2∗: Which MRI sequence to use in the detection of cerebral microbleeds? The Karolinska Imaging Dementia Study},
   volume = {36},
   year = {2015},
}
@article{Cheng2013,
   abstract = {Background and Purpose-We investigated the sensitivity and reliability of MRI susceptibility-weighted imaging (SWI) compared with routine MRI T2*-weighted gradient-recalled echo (GRE) for cerebral microbleed (CMB) detection. Methods-We used data from a prospective study of cerebral amyloid angiopathy (n=9; mean age, 71±8.3) and healthy non-cerebral amyloid angiopathy controls (n=22; mean age, 68±6.3). Three raters (labeled 1, 2, and 3) independently interpreted the GRE and SWI sequences (using the phase-filtered magnitude image) blinded to clinical information. Results-In 9 cerebral amyloid angiopathy cases, the raters identified 1146 total CMBs on GRE and 1432 CMBs on SWI. In 22 healthy control subjects, the raters identified ≥1 CMBs in 6/22 on GRE (total 9 CMBs) and 5/22 on SWI (total 19 CMBs). Among cerebral amyloid angiopathy cases, the reliability between raters for CMB counts was good for SWI (intraclass correlation coefficient, 0.87) but only moderate for GRE (intraclass correlation coefficient, 0.52). In controls, agreement on the presence or absence of CMBs in controls was moderate to good on both SWI (κ coefficient ranged from 0.57 to 0.74 across the 3 combinations of rater pairs) and GRE (κ range, 0.31 to 0.70). A review of 114 hypointensities identified as possible CMBs indicated that increased detection and reliability on SWI was related to both increased contrast and higher resolution, allowing better discrimination of CMBs from the background and better anatomic differentiation from pial vessels. Conclusions-SWI confers greater reliability as well as greater sensitivity for CMB detection compared with GRE, and should be the preferred sequence for quantifying CMB counts. © 2013 American Heart Association, Inc.},
   author = {Ah Ling Cheng and Saima Batool and Cheryl R. McCreary and M. L. Lauzon and Richard Frayne and Mayank Goyal and Eric E. Smith},
   doi = {10.1161/STROKEAHA.113.002267},
   issn = {00392499},
   issue = {10},
   journal = {Stroke},
   keywords = {Cerebral hemorrhage,Magnetic resonance imaging,Microbleeds},
   month = {10},
   pages = {2782-2786},
   pmid = {23920014},
   title = {Susceptibility-weighted imaging is more reliable than T2*-weighted gradient-recalled echo mri for detecting microbleeds},
   volume = {44},
   year = {2013},
}
@generic{Greenberg2009,
   abstract = {Cerebral microbleeds (CMBs) are increasingly recognised neuroimaging findings in individuals with cerebrovascular disease and dementia, and in normal ageing. There has been substantial progress in the understanding of CMBs in recent years, particularly in the development of newer MRI methods for the detection of CMBs and the application of these techniques to population-based samples of elderly people. In this Review, we focus on these recent developments and their effects on two main questions: how CMBs are detected, and how CMBs should be interpreted. The number of CMBs detected depends on MRI characteristics, such as pulse sequence, sequence parameters, spatial resolution, magnetic field strength, and image post-processing, emphasising the importance of taking into account MRI technique in the interpretation of study results. Recent investigations with sensitive MRI techniques have indicated a high prevalence of CMBs in community-dwelling elderly people. We propose a procedural guide for identification of CMBs and suggest possible future approaches for elucidating the role of these common lesions as markers for, and contributors to, small-vessel brain disease. © 2009 Elsevier Ltd. All rights reserved.},
   author = {Steven M. Greenberg and Meike W. Vernooij and Charlotte Cordonnier and Anand Viswanathan and Rustam Al-Shahi Salman and Steven Warach and Lenore J. Launer and Mark A. Van Buchem and Monique MB Breteler},
   doi = {10.1016/S1474-4422(09)70013-4},
   issn = {14744422},
   issue = {2},
   journal = {The Lancet Neurology},
   month = {2},
   pages = {165-174},
   pmid = {19161908},
   title = {Cerebral microbleeds: a guide to detection and interpretation},
   volume = {8},
   year = {2009},
}
@report{Fazekas1999,
   abstract = {BACKGROUND AND PURPOSE: Patients with spontaneous intracerebral hemorrhage (ICH) frequently have small areas of signal loss on gradient-echo T2*-weighted MR images, which have been suggested to represent remnants of previous microbleeds. Our aim was to provide histopathologic support for this assumption and to clarify whether the presence and location of microbleeds were associated with microangiopathy. METHODS: We performed MR imaging and correlative histopathologic examination in 11 formalin-fixed brains of patients who had died of an ICH (age range, 45-90 years). RESULTS: Focal areas of signal loss on MR images were noted in seven brains. They were seen in a corticosubcortical location in six brains, in the basal ganglia/thalami in five, and infratentorially in three specimens. Histopathologic examination showed focal hemosiderin de-position in 21 of 34 areas of MR signal loss. No other corresponding abnormalities were found; however, hemosiderin deposits were noted without MR signal changes in two brains. All specimens with MR foci of signal loss showed moderate to severe fibrohyalinosis, and there was additional evidence of amyloid angiopathy in two of those brains. CONCLUSION: Small areas of signal loss on gradient echo T2*-weighted images indicate previous extravasation of blood and are related to bleeding-prone microangiopathy of different origins.},
   author = {Franz Fazekas and Reinhold Kleinert and Gudrun Roob and Gertrude Kleinert and Peter Kapeller and Reinhold Schmidt and Hans-Peter Hartung},
   journal = {AJNR Am J Neuroradiol},
   pages = {637-642},
   title = {Histopathologic Analysis of Foci of Signal Loss on Gradient-Echo T2*-Weighted MR Images in Patients with Spontaneous Intracerebral Hemorrhage: Evidence of Microangiopathy-Related Microbleeds},
   volume = {20},
   year = {1999},
}
@article{Ingala2020,
   abstract = {Positive associations between cerebral microbleeds (CMBs) and APOE-ε4 (apolipoprotein E) genotype have been reported in Alzheimer's disease, but show conflicting results. We investigated the effect of APOE genotype on CMBs in a cohort of cognitively unimpaired middle- and old-aged individuals enriched for APOE-ε4 genotype. Participants from ALFA (Alzheimer and Families) cohort were included and their magnetic resonance scans assessed (n = 564, 50% APOE-ε4 carriers). Quantitative magnetic resonance analyses included visual ratings, atrophy measures, and white matter hyperintensity (WMH) segmentations. The prevalence of CMBs was 17%, increased with age (p < 0.05), and followed an increasing trend paralleling APOE-ε4 dose. The number of CMBs was significantly higher in APOE-ε4 homozygotes compared to heterozygotes and non-carriers (p < 0.05). This association was driven by lobar CMBs (p < 0.05). CMBs co-localized with WMH (p < 0.05). No associations between CMBs and APOE-ε2, gray matter volumes, and cognitive performance were found. Our results suggest that cerebral vessels of APOE-ε4 homozygous are more fragile, especially in lobar locations. Co-occurrence of CMBs and WMH suggests that such changes localize in areas with increased vascular vulnerability.},
   author = {Silvia Ingala and Linda Mazzai and Carole H. Sudre and Gemma Salvadó and Anna Brugulat-Serrat and Viktor Wottschel and Carles Falcon and Grégory Operto and Betty Tijms and Juan Domingo Gispert and José Luis Molinuevo and Frederik Barkhof},
   doi = {10.1016/j.neurobiolaging.2020.06.015},
   issn = {15581497},
   journal = {Neurobiology of Aging},
   keywords = {APOE,Alzheimer's disease (AD),Cerebral microbleeds (CMBs),Magnetic resonance imaging (MRI),White matter hyperintensities (WMH)},
   month = {11},
   pages = {104-114},
   pmid = {32791423},
   publisher = {Elsevier Inc.},
   title = {The relation between APOE genotype and cerebral microbleeds in cognitively unimpaired middle- and old-aged individuals},
   volume = {95},
   year = {2020},
}
@web_page{Geneva2020,
   abstract = {the top 10 causes of death accounted for 55% of the 55.4 million deaths worldwide. The top global causes of death, in order of total number of lives lost, are associated with three broad topics: cardiovascular (ischaemic heart disease, stroke), respiratory (chronic obstructive pulmonary disease, lower respiratory infections) and neonatal conditions-which include birth asphyxia and birth trauma, neonatal sepsis and infections, and preterm birth complications. Causes of death can be grouped into three categories: communicable (infectious and parasitic diseases and maternal, perinatal and nutritional conditions), noncommunicable (chronic) and injuries. Leading causes of death globally At a global level, 7 of the 10 leading causes of deaths in 2019 were noncommunicable diseases. These seven causes accounted for 44% of all deaths or 80% of the top 10. However, all noncommunicable diseases together accounted for 74% of deaths globally in 2019.},
   author = {World Health Organization Geneva},
   title = {Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019},
   url = {https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death},
   year = {2020},
}
@generic{Haller2018,
   abstract = {Cerebral microbleeds (CMBs), also referred to as microhemorrhages, appear on magnetic resonance (MR) images as hypointense foci notably at T2∗-weighted or susceptibility-weighted (SW) imaging. CMBs are detected with increasing frequency because of the more widespread use of high magnetic field strength and of newer dedicated MR imaging techniques such as three-dimensional gradient-echo T2∗-weighted and SW imaging. The imaging appearance of CMBs is mainly because of changes in local magnetic susceptibility and reflects the pathologic iron accumulation, most often in perivascular macrophages, because of vasculopathy. CMBs are depicted with a truepositive rate of 48%-89% at 1.5 T or 3.0 T and T2∗-weighted or SW imaging across a wide range of diseases. False-positive "mimics" of CMBs occur at a rate of 11%- 24% and include microdissections, microaneurysms, and microcalcifications; the latter can be differentiated by using phase images. Compared with postmortem histopathologic analysis, at least half of CMBs are missed with premortem clinical MR imaging. In general, CMB detection rate increases with field strength, with the use of threedimensional sequences, and with postprocessing methods that use local perturbations of the MR phase to enhance T2∗ contrast. Because of the more widespread availability of high-field-strength MR imaging systems and growing use of SW imaging, CMBs are increasingly recognized in normal aging, and are even more common in various disorders such as Alzheimer dementia, cerebral amyloid angiopathy, stroke, and trauma. Rare causes include endocarditis, cerebral autosomal dominant arteriopathy with subcortical infarcts, leukoencephalopathy, and radiation therapy. The presence of CMBs in patients with stroke is increasingly recognized as a marker of worse outcome. Finally, guidelines for adjustment of anticoagulant therapy in patients with CMBs are under development.},
   author = {Sven Haller and Meike W. Vernooij and Joost P.A. Kuijer and Elna Marie Larsson and Hans Rolf Jäger and Frederik Barkhof},
   doi = {10.1148/radiol.2018170803},
   issn = {15271315},
   issue = {1},
   journal = {Radiology},
   month = {4},
   pages = {11-28},
   pmid = {29558307},
   publisher = {Radiological Society of North America Inc.},
   title = {Cerebral microbleeds: Imaging and clinical significance},
   volume = {287},
   year = {2018},
}
@article{Menze2015,
   abstract = {In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.},
   author = {Bjoern H. Menze and Andras Jakab and Stefan Bauer and Jayashree Kalpathy-Cramer and Keyvan Farahani and Justin Kirby and Yuliya Burren and Nicole Porz and Johannes Slotboom and Roland Wiest and Levente Lanczi and Elizabeth Gerstner and Marc André Weber and Tal Arbel and Brian B. Avants and Nicholas Ayache and Patricia Buendia and D. Louis Collins and Nicolas Cordier and Jason J. Corso and Antonio Criminisi and Tilak Das and Hervé Delingette and Çağatay Demiralp and Christopher R. Durst and Michel Dojat and Senan Doyle and Joana Festa and Florence Forbes and Ezequiel Geremia and Ben Glocker and Polina Golland and Xiaotao Guo and Andac Hamamci and Khan M. Iftekharuddin and Raj Jena and Nigel M. John and Ender Konukoglu and Danial Lashkari and José António Mariz and Raphael Meier and Sérgio Pereira and Doina Precup and Stephen J. Price and Tammy Riklin Raviv and Syed M.S. Reza and Michael Ryan and Duygu Sarikaya and Lawrence Schwartz and Hoo Chang Shin and Jamie Shotton and Carlos A. Silva and Nuno Sousa and Nagesh K. Subbanna and Gabor Szekely and Thomas J. Taylor and Owen M. Thomas and Nicholas J. Tustison and Gozde Unal and Flor Vasseur and Max Wintermark and Dong Hye Ye and Liang Zhao and Binsheng Zhao and Darko Zikic and Marcel Prastawa and Mauricio Reyes and Koen Van Leemput},
   doi = {10.1109/TMI.2014.2377694},
   issn = {1558254X},
   issue = {10},
   journal = {IEEE Transactions on Medical Imaging},
   keywords = {Benchmark,Brain,Image segmentation,MRI,Oncology/tumor},
   month = {10},
   pages = {1993-2024},
   pmid = {25494501},
   publisher = {Institute of Electrical and Electronics Engineers Inc.},
   title = {The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)},
   volume = {34},
   year = {2015},
}
@article{Sudre2019,
   abstract = {Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multirater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency.},
   author = {Carole H. Sudre and Beatriz Gomez Anson and Silvia Ingala and Chris D. Lane and Daniel Jimenez and Lukas Haider and Thomas Varsavsky and Ryutaro Tanno and Lorna Smith and Sébastien Ourselin and Rolf H. Jäger and M. Jorge Cardoso},
   month = {9},
   title = {Let's agree to disagree: learning highly debatable multirater labelling},
   url = {http://arxiv.org/abs/1909.01891},
   year = {2019},
}
@article{Matsoukas2022,
   abstract = {Background: Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. Methods: In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. Results: In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. Conclusions: Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. Registration-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.},
   author = {Stavros Matsoukas and Jacopo Scaggiante and Braxton R. Schuldt and Colton J. Smith and Susmita Chennareddy and Roshini Kalagara and Shahram Majidi and Joshua B. Bederson and Johanna T. Fifi and J. Mocco and Christopher P. Kellner},
   doi = {10.1007/s11547-022-01530-4},
   issn = {18266983},
   issue = {10},
   journal = {Radiologia Medica},
   keywords = {AI-assisted diagnosis,Artificial intelligence,Chronic microbleeds,Convolutional neural network,Intracranial hemorrhage,Non-contrast CT scan},
   month = {10},
   pages = {1106-1123},
   pmid = {35962888},
   publisher = {Springer-Verlag Italia s.r.l.},
   title = {Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis},
   volume = {127},
   year = {2022},
}
@generic{Tsuneki2022,
   abstract = {Background: Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice. Highlight: Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry. Conclusion: The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.},
   author = {Masayuki Tsuneki},
   doi = {10.1016/j.job.2022.03.003},
   issn = {18803865},
   issue = {3},
   journal = {Journal of Oral Biosciences},
   keywords = {Artificial intelligence,Computer vision,Computer-aided diagnosis,Deep learning,Medical image analysis},
   month = {9},
   pages = {312-320},
   pmid = {35306172},
   publisher = {Japanese Association for Oral Biology},
   title = {Deep learning models in medical image analysis},
   volume = {64},
   year = {2022},
}
@book{colliot2023machine,
   author = {Olivier Colliot},
   publisher = {Springer Nature},
   title = {Machine Learning for Brain Disorders},
   url = {http://www.springer.com/series/7657},
   year = {2023},
}
@article{MetricsReloaded,
   abstract = {Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases.},
   author = {Lena Maier-Hein and Annika Reinke and Patrick Godau and Minu D. Tizabi and Florian Buettner and Evangelia Christodoulou and Ben Glocker and Fabian Isensee and Jens Kleesiek and Michal Kozubek and Mauricio Reyes and Michael A. Riegler and Manuel Wiesenfarth and A. Emre Kavur and Carole H. Sudre and Michael Baumgartner and Matthias Eisenmann and Doreen Heckmann-Nötzel and A. Tim Rädsch and Laura Acion and Michela Antonelli and Tal Arbel and Spyridon Bakas and Arriel Benis and Matthew Blaschko and M. Jorge Cardoso and Veronika Cheplygina and Beth A. Cimini and Gary S. Collins and Keyvan Farahani and Luciana Ferrer and Adrian Galdran and Bram van Ginneken and Robert Haase and Daniel A. Hashimoto and Michael M. Hoffman and Merel Huisman and Pierre Jannin and Charles E. Kahn and Dagmar Kainmueller and Bernhard Kainz and Alexandros Karargyris and Alan Karthikesalingam and Hannes Kenngott and Florian Kofler and Annette Kopp-Schneider and Anna Kreshuk and Tahsin Kurc and Bennett A. Landman and Geert Litjens and Amin Madani and Klaus Maier-Hein and Anne L. Martel and Peter Mattson and Erik Meijering and Bjoern Menze and Karel G. M. Moons and Henning Müller and Brennan Nichyporuk and Felix Nickel and Jens Petersen and Nasir Rajpoot and Nicola Rieke and Julio Saez-Rodriguez and Clara I. Sánchez and Shravya Shetty and Maarten van Smeden and Ronald M. Summers and Abdel A. Taha and Aleksei Tiulpin and Sotirios A. Tsaftaris and Ben Van Calster and Gaël Varoquaux and Paul F. Jäger},
   month = {6},
   title = {Metrics reloaded: Pitfalls and recommendations for image analysis validation},
   url = {http://arxiv.org/abs/2206.01653},
   year = {2022},
}
@article{,
   abstract = {Nature | www.nature.com | 1 Article Variability in the analysis of a single neuroimaging dataset by many teams Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses 1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset 2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. Data analysis workflows in many areas of science have a large number of analysis steps that involve many possible choices (that is, "researcher degrees of freedom" 6,7). Simulation studies show that variability in analytical choices can have substantial effects on results 8 , but its degree and effect in practice is unclear. Recent work in psychology addressed this through a "many analysts" approach 9 , in which the same dataset was analysed by a large number of groups, uncovering substantial variability in behavioural results across analysis teams. In the Neuroim-aging Analysis Replication and Prediction Study (NARPS), we applied a similar approach to the domain of functional magnetic resonance imaging (fMRI), the analysis workflows of which are complex and highly variable. Our goal was to assess-with the highest possible ecological validity-the degree and effect of analytical flexibility on fMRI results in practice. In addition, we estimated the beliefs of researchers in the field regarding the degree of variability in analysis outcomes using prediction markets to test whether peers in the field could predict the results 2-5 .},
   author = {Rotem Botvinik-Nezer and Felix Holzmeister and Colin F Camerer and Anna Dreber and Juergen Huber and Magnus Johannesson and Michael Kirchler and Roni Iwanir and Jeanette A Mumford and R Alison Adcock and Paolo Avesani and Blazej M Baczkowski and Aahana Bajracharya and Leah Bakst and Sheryl Ball and Marco Barilari and ge Bault and Derek Beaton and Julia Beitner and Roland G Benoit and Ruud M W J Berkers and Jamil P Bhanji and Bharat B Biswal and Sebastian Bobadilla-Suarez and Tiago Bortolini and Katherine L Bottenhorn and Alexander Bowring and Senne Braem and Hayley R Brooks and Emily G Brudner and Cristian B Calderon and Julia A Camilleri and Jaime J Castrellon and Luca Cecchetti and Edna C Cieslik and Zachary J Cole and Olivier Collignon and Robert W Cox and William A Cunningham and Stefan Czoschke and Kamalaker Dadi and Charles P Davis and Alberto De Luca and Mauricio R Delgado and Lysia Demetriou and Jeffrey B Dennison and Xin Di and Erin W Dickie and Ekaterina Dobryakova and Claire L Donnat and Juergen Dukart and Niall W Duncan and Joke Durnez and Amr Eed and Simon B Eickhoff and Andrew Erhart and Laura Fontanesi and G Matthew Fricke and Shiguang Fu and Adriana Galvamp and Remi Gau and Sarah Genon and Tristan Glatard and Enrico Glerean and Jelle J Goeman and Sergej A E Golowin and Carlos Gonzamp and Krzysztof J Gorgolewski and Cheryl L Grady and Mikella A Green and F Guassi Moreira and Olivia Guest and Shabnam Hakimi and J Paul Hamilton and Roeland Hancock and Giacomo Handjaras and Bronson B Harry and Colin Hawco and Peer Herholz and Gabrielle Herman and Stephan Heunis and Felix Hoffstaedter and Jeremy Hogeveen and Susan Holmes and Chuan-Peng Hu and Scott A Huettel and Matthew E Hughes and Vittorio Iacovella and Alexandru D Iordan and Peder M Isager and Ayse I Isik and Andrew Jahn and Matthew R Johnson and Tom Johnstone and Michael J E Joseph and Anthony C Juliano and Joseph W Kable and Michalis Kassinopoulos and Cemal Koba and Xiang-Zhen Kong and Timothy R Koscik and Nuri Erkut Kucukboyaci and Brice A Kuhl and Sebastian Kupek and Angela R Laird and Claus Lamm and Robert Langner and Nina Lauharatanahirun and Hongmi Lee and Sangil Lee and Alexander Leemans and Andrea Leo and Elise Lesage and Flora Li and Monica Y C Li and Phui Cheng Lim and Evan N Lintz and Schuyler W Liphardt and Annabel B Losecaat Vermeer and Bradley C Love and Michael L Mack and Norberto Malpica and Theo Marins and Camille Maumet and Kelsey McDonald and Joseph T McGuire and Helena Melero and Adriana S Mamp and ndez Leal and Benjamin Meyer and Kristin N Meyer and Glad Mihai and Georgios D Mitsis and Jorge Moll and Dylan M Nielson and Gustav Nilsonne and Michael P Notter and Emanuele Olivetti and Adrian I Onicas and Paolo Papale and Kaustubh R Patil and Jonathan E Peelle and Alexandre Pamp and Doris Pischedda and Jean-Baptiste Poline and Yanina Prystauka and Shruti Ray and Patricia A Reuter-Lorenz and Richard C Reynolds and Emiliano Ricciardi and Jenny R Rieck and Anais M Rodriguez-Thompson and Anthony Romyn and Taylor Salo and Gregory R Samanez-Larkin and Emilio Sanz-Morales and Margaret L Schlichting and Douglas H Schultz and Qiang Shen and Margaret A Sheridan and Jennifer A Silvers and Kenny Skagerlund and Alec Smith and David V Smith and Peter Sokol-Hessner and Simon R Steinkamp and Sarah M Tashjian and Bertrand Thirion and John N Thorp and Gustav Tinghamp and Loreen Tisdall and Steven H Tompson and Claudio Toro-Serey and Juan Jesus Torre Tresols and Leonardo Tozzi and Vuong Truong and Luca Turella and Anna E van amp and Tom Verguts and Jean M Vettel and Sagana Vijayarajah and Khoi Vo and Matthew B Wall and Wouter D Weeda and Susanne Weis and David J White and David Wisniewski and Alba Xifra-Porxas and Emily A Yearling and Sangsuk Yoon and Rui Yuan and Kenneth S L Yuen and Lei Zhang and Xu Zhang and Joshua E Zosky and Thomas E Nichols and Russell A Poldrack and Tom Schonberg},
   doi = {10.18112/openneuro},
   title = {Variability in the analysis of a single neuroimaging dataset by many teams},
   url = {https://doi.org/10.18112/openneuro.},
   year = {2020},
}
@article{Tajbakhsh2020,
   abstract = {The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions. We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.},
   author = {Nima Tajbakhsh and Laura Jeyaseelan and Qian Li and Jeffrey N. Chiang and Zhihao Wu and Xiaowei Ding},
   doi = {10.1016/j.media.2020.101693},
   issn = {13618423},
   journal = {Medical Image Analysis},
   keywords = {And weak annotations,Imperfect dataset,Medical image segmentation,Noisy annotations,Scarce annotations,Sparse annotations,Unreliable annotations},
   month = {7},
   pmid = {32289663},
   publisher = {Elsevier B.V.},
   title = {Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation},
   volume = {63},
   year = {2020},
}
@article{Horien2021,
   abstract = {Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.},
   author = {Corey Horien and Stephanie Noble and Abigail S. Greene and Kangjoo Lee and Daniel S. Barron and Siyuan Gao and David O’Connor and Mehraveh Salehi and Javid Dadashkarimi and Xilin Shen and Evelyn M.R. Lake and R. Todd Constable and Dustin Scheinost},
   doi = {10.1038/s41562-020-01005-4},
   issn = {23973374},
   issue = {2},
   journal = {Nature Human Behaviour},
   month = {2},
   pages = {185-193},
   pmid = {33288916},
   publisher = {Nature Research},
   title = {A hitchhiker’s guide to working with large, open-source neuroimaging datasets},
   volume = {5},
   year = {2021},
}
@report{Shukla2023,
   abstract = {High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally. In many cases, these weak labels dictate the frequency of each respective class over a set of instances. In this paper, we develop a unified approach to learning from such weakly-labeled data, which we call count-based weakly-supervised learning. At the heart of our approach is the ability to compute the probability of exactly k out of n outputs being set to true. This computation is differentiable, exact, and efficient. Building upon the previous computation, we derive a count loss penalizing the model for deviations in its distribution from an arithmetic constraint defined over label counts. We evaluate our approach on three common weakly-supervised learning paradigms and observe that our proposed approach achieves state-of-the-art or highly competitive results across all three of the paradigms.},
   author = {Vinay Shukla and Zhe Zeng and Kareem Ahmed and Guy Van Den Broeck},
   title = {A Unified Approach to Count-Based Weakly-Supervised Learning},
   year = {2023},
}
@generic{Ferlin2023,
   abstract = {This paper provides the first review to date which gathers, describes, and assesses, to the best of our knowledge, all available publications on automating cerebral microbleed (CMB) detection. It provides insights into the current state of the art and highlights the challenges and opportunities in this topic. By incorporating the best practices identified in this review, we established guidelines for the development of CMB detection systems. We are confident that these guidelines can serve as a foundation for further research. CMB detection is a crucial but challenging task that can be laborious for radiologists. With the increasing popularity of magnetic resonance imaging (MRI), the ability to detect CMBs has improved, but there is still a need to automate this process to enhance its efficiency and accuracy. A high prevalence of CMBs is closely associated with cognitive dysfunction, diabetes, hypertension, an increased risk of stroke, and intracerebral hemorrhage. It is alarming to note that strokes, Alzheimer's disease, and Diabetes mellitus have secured their position as the second, seventh, and ninth most common causes of death worldwide, respectively. Moreover, CMBs are sometimes found in association with other pathologies and indicate a range of pathological processes in the cerebral vessels. Thus, it is essential to enhance the quality of diagnostics to facilitate prompt identification and treatment of these potentially life-threatening conditions. In this paper, we aimed to systematize the existing knowledge and best practices in automatic CMB detection, from fundamental information about CMBs and MRI image data, through employed datasets and CMB detection and verification algorithms, to methods of result evaluation. This can serve as a starting point for future research and the development of a CMB detection system that is practically applicable in medicine, leading to enhanced patient treatment outcomes.},
   author = {Maria Ferlin and Zuzanna Klawikowska and Michał Grochowski and Małgorzata Grzywińska and Edyta Szurowska},
   doi = {10.1016/j.eswa.2023.120655},
   issn = {09574174},
   journal = {Expert Systems with Applications},
   keywords = {Automatic diagnosis,CMB,Cerebral microbleeds,Classification,Computer-aided diagnosis,Detection,Segmentation},
   month = {12},
   publisher = {Elsevier Ltd},
   title = {Exploring the landscape of automatic cerebral microbleed detection: A comprehensive review of algorithms, current trends, and future challenges},
   volume = {232},
   year = {2023},
}
@generic{Jiang2022,
   abstract = {Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.},
   author = {Jiyang Jiang and Dadong Wang and Yang Song and Perminder S. Sachdev and Wei Wen},
   doi = {10.1016/j.neuroimage.2022.119528},
   issn = {10959572},
   journal = {NeuroImage},
   keywords = {Artificial intelligence,Cerebral microbleeds,Cerebral small vessel disease,Computer-aided segmentation,Dilated perivascular spaces,Lacunes of presumed vascular origin},
   month = {11},
   pmid = {35914668},
   publisher = {Academic Press Inc.},
   title = {Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review},
   volume = {261},
   year = {2022},
}
@article{Ma2023,
   abstract = {Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, current methods predominantly rely on customized models, which exhibit limited generality across diverse tasks. In this study, we present MedSAM, the inaugural foundation model designed for universal medical image segmentation. Harnessing the power of a meticulously curated dataset comprising over one million images, MedSAM not only outperforms existing state-of-the-art segmentation foundation models, but also exhibits comparable or even superior performance to specialist models. Moreover, MedSAM enables the precise extraction of essential biomarkers for tumor burden quantification. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.},
   author = {Jun Ma and Yuting He and Feifei Li and Lin Han and Chenyu You and Bo Wang},
   month = {4},
   title = {Segment Anything in Medical Images},
   url = {http://arxiv.org/abs/2304.12306},
   year = {2023},
}
@article{Sundaresan2023,
   abstract = {Introduction: Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods: In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results: On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.},
   author = {Vaanathi Sundaresan and Christoph Arthofer and Giovanna Zamboni and Andrew G. Murchison and Robert A. Dineen and Peter M. Rothwell and Dorothee P. Auer and Chaoyue Wang and Karla L. Miller and Benjamin C. Tendler and Fidel Alfaro-Almagro and Stamatios N. Sotiropoulos and Nikola Sprigg and Ludovica Griffanti and Mark Jenkinson},
   doi = {10.3389/fninf.2023.1204186},
   issn = {16625196},
   journal = {Frontiers in Neuroinformatics},
   keywords = {cerebral microbleed (CMB),deep learning,detection,knowledge distillation,magnetic resonance imaging,quantitative susceptibility mapping (QSM),susceptibility-weighted image (SWI)},
   publisher = {Frontiers Media SA},
   title = {Automated detection of cerebral microbleeds on MR images using knowledge distillation framework},
   volume = {17},
   year = {2023},
}
@article{Simko2022,
   abstract = {Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be improved if we also employ empirical rigor that incorporates reproducibility guidelines, especially so in the medical imaging field. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in this direction by advocating open access, and recently also recommending authors to make their code public - both aspects being adopted by the majority of the conference submissions. This helps the reproducibility of the methods, however, there is currently little or no support for further evaluation of these supplementary material, making them vulnerable to poor quality, which affects the impact of the entire submission. We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but slightly adjusted guidelines on reproducibility and the quality of the public repositories. The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories has not improved over the years, leaving room for improvement in every aspect of designing repositories. Merely 22% of all submissions contain a repository that were deemed repeatable using our evaluations. From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL.},
   author = {Attila Simko and Anders Garpebring and Joakim Jonsson and Tufve Nyholm and Tommy Löfstedt},
   month = {10},
   title = {Reproducibility of the Methods in Medical Imaging with Deep Learning},
   url = {http://arxiv.org/abs/2210.11146},
   year = {2022},
}
@article{Chesebro2021,
   abstract = {Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral amyloid angiopathy, and Alzheimer’s disease. The current gold standard for detecting and rating cerebral microbleeds in a research context is visual inspection by trained raters, a process that is both time consuming and subject to poor reliability. We present here a novel method to automate microbleed detection on GRE and SWI images. We demonstrate in a community-based cohort of older adults that the method is highly sensitive (greater than 92% of all microbleeds accurately detected) across both modalities, with reasonable precision (fewer than 20 and 10 false positives per scan on GRE and SWI, respectively). We also demonstrate that the algorithm can be used to identify microbleeds over longitudinal scans with a higher level of sensitivity than visual ratings (50% of longitudinal microbleeds correctly labeled by the algorithm, while manual ratings was 30% or lower). Further, the algorithm identifies the anatomical localization of microbleeds based on brain atlases, and greatly reduces time spent completing visual ratings (43% reduction in visual rating time). Our automatic microbleed detection instrument is ideal for implementation in large-scale studies that include cross-sectional and longitudinal scanning, as well as being capable of performing well across multiple commonly used MRI modalities.},
   author = {Anthony G. Chesebro and Erica Amarante and Patrick J. Lao and Irene B. Meier and Richard Mayeux and Adam M. Brickman},
   doi = {10.1038/s41598-021-83607-0},
   issn = {20452322},
   issue = {1},
   journal = {Scientific Reports},
   month = {12},
   pmid = {33597663},
   publisher = {Nature Research},
   title = {Automated detection of cerebral microbleeds on T2*-weighted MRI},
   volume = {11},
   year = {2021},
}
@generic{Chojdak2021,
   abstract = {Cerebral small vessel disease (CSVD) is the most common, chronic and progressive vascular disease. The changes affect arterioles, capillaries and small veins supplying the white matter and deep structures of the brain. It is the most common incidental finding on brain scans, especially in people over 80 years of age. Magnetic resonance imaging (MRI) plays a key role in the diagnosis of CSVD. The nomenclature and radiological phenotypes of CSVD were published in 2013 based on the unified position of the so-called Centres of Excellence in Neurodegeneration. The disease is characterized by a diverse clinical and radiological picture. It is primarily responsible for stroke incidents, gait disturbances, depression, cognitive impairment, and dementia in the elderly. The CSVD contributes to about 20% of strokes, including 25% of ischemic strokes and 45% of dementias. Common causes of CSVD include arteriosclerosis, cerebral amyloid angiopathy (CAA), genetic small vessel angiopathy, inflammation and immune-mediated small vessel diseases, and venous collagenosis. There is no causal treatment and management is mainly based on combating known risk factors for cardiovascular disease (CVD).},
   author = {Justyna Chojdak-Łukasiewicz and Edyta Dziadkowiak and Anna Zimny and Bogusław Paradowski},
   doi = {10.17219/ACEM/131216},
   issn = {24512680},
   issue = {3},
   journal = {Advances in Clinical and Experimental Medicine},
   keywords = {Amyloidosis,Cerebral small vessel disease,Lacunar infarcts,Microbleeds,White matter hyperintensities},
   month = {3},
   pages = {349-356},
   pmid = {33768739},
   publisher = {Wroclaw University of Medicine},
   title = {Cerebral small vessel disease: A review},
   volume = {30},
   year = {2021},
}
@article{Sudre2022,
   abstract = {Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit\{VAscular Lesions DetectiOn and Segmentation\} (\textit\{Where is VALDO?\}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.},
   author = {Carole H. Sudre and Kimberlin Van Wijnen and Florian Dubost and Hieab Adams and David Atkinson and Frederik Barkhof and Mahlet A. Birhanu and Esther E. Bron and Robin Camarasa and Nish Chaturvedi and Yuan Chen and Zihao Chen and Shuai Chen and Qi Dou and Tavia Evans and Ivan Ezhov and Haojun Gao and Marta Girones Sanguesa and Juan Domingo Gispert and Beatriz Gomez Anson and Alun D. Hughes and M. Arfan Ikram and Silvia Ingala and H. Rolf Jaeger and Florian Kofler and Hugo J. Kuijf and Denis Kutnar and Minho Lee and Bo Li and Luigi Lorenzini and Bjoern Menze and Jose Luis Molinuevo and Yiwei Pan and Elodie Puybareau and Rafael Rehwald and Ruisheng Su and Pengcheng Shi and Lorna Smith and Therese Tillin and Guillaume Tochon and Helene Urien and Bas H. M. van der Velden and Isabelle F. van der Velpen and Benedikt Wiestler and Frank J. Wolters and Pinar Yilmaz and Marius de Groot and Meike W. Vernooij and Marleen de Bruijne},
   month = {8},
   title = {Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021},
   url = {http://arxiv.org/abs/2208.07167},
   year = {2022},
}
@article{Koschmieder2022,
   abstract = {Cerebral microbleeds (CMBs) are a recognised biomarker of traumatic axonal injury (TAI). Their number and location provide valuable information in the long-term prognosis of patients who sustained a traumatic brain injury (TBI). Accurate detection of CMBs is necessary for both research and clinical applications. CMBs appear as small hypointense lesions on susceptibility-weighted magnetic resonance imaging (SWI). Their size and shape vary markedly in cases of TBI. Manual annotation of CMBs is a difficult, error-prone, and time-consuming task. Several studies addressed the detection of CMBs in other neuropathologies with convolutional neural networks (CNNs). In this study, we developed and contrasted a classification (Patch-CNN) and two segmentation (Segmentation-CNN, U-Net) approaches for the detection of CMBs in TBI cases. The models were trained using 45 datasets, and the best models were chosen according to 16 validation sets. Finally, the models were evaluated on 10 TBI and healthy control cases, respectively. Our three models outperform the current status quo in the detection of traumatic CMBs, achieving higher sensitivity at low false positive (FP) counts. Furthermore, using a segmentation approach allows for better precision. The best model, the U-Net, achieves a detection rate of 90% at FP counts of 17.1 in TBI patients and 3.4 in healthy controls.},
   author = {K. Koschmieder and M. M. Paul and T. L.A. van den Heuvel and A. W. van der Eerden and B. van Ginneken and R. Manniesing},
   doi = {10.1016/j.nicl.2022.103027},
   issn = {22131582},
   journal = {NeuroImage: Clinical},
   keywords = {Cerebral Microbleeds,Computer aided detection,Convolutional neural networks,Deep learning,Susceptibility weighted imaging,Traumatic brain injury},
   month = {1},
   pmid = {35597029},
   publisher = {Elsevier Inc.},
   title = {Automated detection of cerebral microbleeds via segmentation in susceptibility-weighted images of patients with traumatic brain injury},
   volume = {35},
   year = {2022},
}