Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects. In this work, we propose a multi-stage approach to both microbleed detection and segmentation. First, possible microbleed locations are detected with a Mask R-CNN technique. Second, at each possible microbleed location, a simple U-Net performs the final segmentation. This work used the 72 subjects as training data provided by the "Where is VALDO?" challenge of MICCAI 2021.
We propose a novel framework for controllable pathological image synthesis for data augmentation. Inspired by CycleGAN, we perform cycle-consistent image-to-image translation between two domains: healthy and pathological. Guided by a semantic mask, an adversarially trained generator synthesizes pathology on a healthy image in the specified location. We demonstrate our approach on an institutional dataset of cerebral microbleeds in traumatic brain injury patients. We utilize synthetic images generated with our method for data augmentation in cerebral microbleeds detection. Enriching the trainin…
Cerebral microbleeds are small hypointense lesions visible on magnetic resonance imaging (MRI) with gradient echo, T2*, or susceptibility weighted (SWI) imaging. Assessment of cerebral microbleeds is mostly performed by visual inspection. The past decade has seen the rise of semi-automatic tools to assist with rating and more recently fully automatic tools for microbleed detection. In this work, we explore the use of nnU-Net as a fully automated tool for microbleed segmentation. Data was provided by the ``Where is VALDO?'' challenge of MICCAI 2021. The final method consists of nnU-Net in the `…
University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital（哥本哈根大学医院）
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 limit…
University of Pennsylvania（宾夕法尼亚大学）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Bern（伯尔尼大学）；Weill Cornell Medical College（威尔康奈尔医学院）；Boston University（波士顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Washington（华盛顿大学）
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenien…
Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning
Yonsei University（延世大学）；Gachon University（嘉泉大学）；Carnegie Mellon University（卡内基梅隆大学）；Seoul National University Hospital（首尔大学医院）；Seoul National University College of Medicine（首尔大学医学院）；Daeyang AI Center（大阳AI中心）；Sejong University（世宗大学）
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）；Prince of Wales Hospital（威尔士亲王医院）
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 to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Se…
Xuzhou Medical University（徐州医科大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Suzhou University of Science and Technology（苏州科技大学）
BACKGROUND AND PURPOSE: Cerebral aneurysm is one of the most common cerebrovascular diseases, and SAH caused by its rupture has a very high mortality and disability rate. Existing automatic segmentation methods based on DLMs with TOF-MRA modality could not segment edge voxels very well, so that our goal is to realize more accurate segmentation of cerebral aneurysms in 3D TOF-MRA with the help of DLMs. MATERIALS AND METHODS: In this research, we proposed an automatic segmentation framework of cerebral aneurysm in 3D TOF-MRA. The framework was composed of two segmentation networks ranging from c…
Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）
Automated segmentation of aneurysms from 3D CT is important for the diagnosis, monitoring, and treatment planning of the cerebral aneurysm disease. This short paper briefly presents the main technique details of the aneurysm segmentation method in the MICCAI 2020 CADA challenge. The main contribution is that we configure the 3D U-Net with a large patch size, which can obtain the large context. Our method ranked second on the MICCAI 2020 CADA testing dataset with an average Jaccard of 0.7593. Our code and trained models are publicly available at \url{https://github.com/JunMa11/CADA2020}.
Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Imperial College London（伦敦帝国学院）；Technical University of Munich（慕尼黑工业大学）；University Hospital Zurich（苏黎世大学医院）；Toronto Western Hospital（多伦多西部医院）；Krembil Research Institute（克雷比尔研究所）
The University of Hong Kong（香港大学）；Hospital Authority（医院管理局）
The hyperdense middle cerebral artery (MCA) dot sign has been reported as an important factor in the diagnosis of acute ischemic stroke due to large vessel occlusion. Interpreting the initial CT brain scan in these patients requires high level of expertise, and has high inter-observer variability. An automated computerized interpretation of the urgent CT brain image, with an emphasis to pick up early signs of ischemic stroke will facilitate early patient diagnosis, triage, and shorten the door-to-revascularization time for these group of patients. In this paper, we present an automated detecti…
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学A.I.维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory r…
DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所）；School of Cyber Science and Engineering, Ningbo University of Technology（宁波工程学院网络空间安全学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology（齐鲁工业大学山东省人工智能研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；Erasmus MC, University Medical Center（伊拉斯姆斯大学医学中心）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）；First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；University of Nottingham（诺丁汉大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
Segmentation of cerebral blood vessels from Magnetic Resonance Imaging (MRI) is an open problem that could be solved with deep learning (DL). However, annotated data for training is often scarce. Due to the absence of open-source tools, we aim to develop a classical segmentation method that generates vessel ground truth from Magnetic Resonance Angiography for DL training of segmentation across a variety of modalities. The method combines size-specific Hessian filters, hysteresis thresholding and connected component correction. The optimal choice of processing steps was evaluated with a blinded…
Cerebral hematoma grows rapidly in 6-24 hours and misprediction of the growth can be fatal if it is not operated by a brain surgeon. There are two types of cerebral hematomas: one that grows rapidly and the other that does not grow rapidly. We are developing the technique of artificial intelligence to determine whether the CT image includes the cerebral hematoma which leads to the rapid growth. This problem has various difficulties: the few positive cases in this classification problem of cerebral hematoma and the targeted hematoma has deformable object. Other difficulties include the imbalanc…
Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206（里昂大学、里昂国立应用科学学院、里昂第一大学（克洛德·贝尔纳里昂第一大学）、圣艾蒂安大学、法国国家科学研究中心、法国国家健康与医学研究院、CREATIS联合研究所）；CH Avignon（阿维尼翁中心医院）
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Consequently, it must be detected and monitored by physicians. In clinical routing, manual 2D measurements are performed on 2D ultrasound (US) images to estimate the CVS volume but this practice is imprecise due to the unavailability of 3D information. A way to tackle this problem would be to develop automatic CVS segmentation algorithms for 3D US dat…
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）
More than 13 million people suffer from ischemic cerebral stroke worldwide each year. Thrombolytic treatment can reduce brain damage but has a narrow treatment window. Computed Tomography Perfusion imaging is a commonly used primary assessment tool for stroke patients, and typically the radiologists will evaluate resulting parametric maps to estimate the affected areas, dead tissue (core), and the surrounding tissue at risk (penumbra), to decide further treatments. Different work has been reported, suggesting thresholds, and semi-automated methods, and in later years deep neural networks, for …
Eindhoven University of Technology（埃因霍温理工大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) is an essential step in this process. This study investigates deep learning approaches for automatic, fast and accurate cerebrovascular segmentation for TOF-MRAs. The performance of several data augmentation and selection methods for training a 2D and 3D U-Net for vessel segmentation was investigated in five experiments: a) without augmentation, b) Gaussian blur, c) rotation and …
Accurate segmentation of cerebral vasculature and a quantitative assessment of cerebrovascular morphology is critical to various diagnostic and therapeutic purposes and is pertinent to studying brain health and disease. However, this is still a challenging task due to the complexity of the vascular imaging data. We propose an automated method for cerebral vascular segmentation without the need of any manual intervention as well as a method to skeletonize the binary volume to extract vascular geometric features which can characterize vessel structure. We combine a probabilistic vessel-enhancing…
Academy for Engineering and Technology, Fudan University（复旦大学工程与应用技术研究院）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；Department of Radiology, Huashan Hospital, Fudan University（复旦大学附属华山医院放射科）；Biomedical Engineering, Xuzhou Medical University（徐州医科大学生物医学工程）
Background:Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences.However,if the aneurysm can be found and treated during asymptomatic periods,the probability of rupture can be greatly reduced.At present,time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm,and the application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm.Existing studies have found that three-dimensional features play an important role in aneurysm…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Erasmus MC（伊拉斯姆斯医学中心）；University of Copenhagen（哥本哈根大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer's disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi's vessel enhancement with subsequent…
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada（计算机科学与软件工程系，康科迪亚大学）；NeuroRx Research, Montréal, Canada（NeuroRx研究公司）；Department of Electrical and Computer Engineering, Concordia University, Montréal, Canada（电气与计算机工程系，康科迪亚大学）
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
Faculty of Computer Science, Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校计算机科学学院）；Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校数据与知识工程小组）；Human Technopole（人类技术极地）；Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg（生物医学磁共振，奥托·冯·格里克大学马格德堡分校）；Department of Neurology, Medical Faculty, University Hospital of Magdeburg（马格德堡大学医院医学系神经科）；German Centre for Neurodegenerative Diseases（德国神经退行性疾病研究中心）；Centre for Behavioural Brain Sciences, Magdeburg（行为脑科学中心，马格德堡）；Department of Neurology, University Hospital Zurich（苏黎世大学医院神经科）；Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich（苏黎世大学医院咨询-联络精神病学与心身医学科）；Stanford University（斯坦福大学）；Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（转化放射生物学，放射肿瘤学部，埃尔兰根大学医院，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；National Yang Ming Chiao Tung University（阳明交通大学）；School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；Australian eHealth Research Centre, CSIRO（澳大利亚eHealth研究中心，CSIRO）；National Heart and Lung Institute, Faculty of Medicine, Imperial College London（英国伦敦帝国理工学院医学系国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London（霍克斯研究所，伦敦大学学院计算机科学系）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates（阿布扎克穆罕默德·本·扎耶德人工智能大学）；The Alan Turing Institute, London, UK（艾伦·图灵研究所，伦敦，英国）；School of Computing, University of Leeds（利兹大学计算学院）；Department of Computer Science at School of Informatics, Xiamen University（厦门大学信息学院计算机科学系）；Manteia Technologies Co., Ltd, Xiamen, China（厦门Manteia技术有限公司）；Leicester International Institute, Dalian University of Technology（大连理工大学利兹国际学院）；Sorbonne Université, Institut du Cerveau - Paris Brain Institute（索邦大学，巴黎脑研究所）；Centre of Formation and Research in Artificial Intelligence, Universidad de Los Andes, Colombia（智利洛斯安德斯大学人工智能培训与研究中心）；Data Science Department, EURECOM, Sophia Antipolis, France（EURECOM数据科学系，法国索菲亚安蒂波利斯）
Lausanne University Hospital and University of Lausanne（洛桑大学医院和洛桑大学）；Center for Biomedical Imaging（生物医学成像中心）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called "weak" labels: these can either be coarse or oversized annotations that are less precise, but noticeably faster to create. In this work, we address the task of brain aneurysm detection as a patch-wise binary classification with weak labels, in contrast to related studies that rather use s…
