Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞总医院 Athinoula A. Martinos 生物医学成像中心）；Harvard Medical School（哈佛医学院）；University College London（伦敦大学学院）
Etienne Chollet, Yaël Balbastre, Chiara Mauri, Caroline Magnain, Bruce Fischl, Hui Wang
Microvascular anatomy is known to be involved in various neurological disorders. However, understanding these disorders is hindered by the lack of imaging modalities capable of capturing the comprehensive three-dimensional vascular network structure at microscopic resolution. With a lateral resolution of $<=$20 {\textmu}m and ability to reconstruct large tissue blocks up to tens of cubic centimeters, serial-section optical coherence tomography (sOCT) is well suited for this task. This method uses intrinsic optical properties to visualize the vessels and therefore does not possess a specific contrast, which complicates the extraction of accurate vascular models. The performance of traditional vessel segmentation methods is heavily degraded in the presence of substantial noise and imaging artifacts and is sensitive to domain shifts, while convolutional neural networks (CNNs) require extensive labeled data and are also sensitive the precise intensity characteristics of the data that they are trained on. Building on the emerging field of synthesis-based training, this study demonstrates a synthesis engine for neurovascular segmentation in sOCT images. Characterized by minimal priors and high variance sampling, our highly generalizable method tested on five distinct sOCT acquisitions eliminates the need for manual annotations while attaining human-level precision. Our approach comprises two phases: label synthesis and label-to-image transformation. We demonstrate the efficacy of the former by comparing it to several more realistic sets of training labels, and the latter by an ablation study of synthetic noise and artifact models.
A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image
Sun Yat-sen University（中山大学）；Sichuan University（四川大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets
Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences（脑图谱与脑启发智能实验室，自动化研究所，中国科学院）；State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences（脑认知与脑启发智能技术国家重点实验室，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland；Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Waedenswil, Switzerland；Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland；Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China；Department of Radiology at Weill Cornell Medicine, Cornell University, New York, USA；Institute for Tissue Engineering；School of Computation, Information；Technology, Technical University of Munich, Germany；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA；School of Medicine；Health, TUM Klinikum, Technical University of Munich, Germany；Munich Center for Machine Learning, Munich, Germany；Department of Computing, Imperial College London, London, UK；Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands；Department of Neurology；Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands；Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands；Electronic \& Information Engineering School, Harbin Institute of Technology (Shenzhen), China；Peng Cheng Laboratory, Shenzhen, China；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany；Faculty of Mathematics；Computer Science, Heidelberg University, Germany；Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany；Data Science School for Health, Karlsruhe/Heidelberg, Germany；Learning Group, Department of Radiation Oncology, Heidelberg University Hospital；Department of Radiology, University of Washington, Seattle, WA, USA；Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China；Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland；Department of Neurology, University Hospital of Zurich, Zurich, Switzerland；Department of Physiology, University of Toronto, Canada；Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland；Department of Diagnostic Imaging, National University Hospital, Singapore；University of Chicago, USA；Department of Diagnostic；Interventional Neuroradiology, University Hospital Berne；University of Berne, Berne, Switzerland；Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada；DEEPNOID Inc., Seoul, South Korea；Department of Artificial Intelligence, Korea University, Seoul, South Korea；Charité Lab for AI in Medicine (CLAIM), Charité Universitätsmedizin Berlin, Berlin, Germany；Lung Institute, Faculty of Medicine, Imperial College London, London, UK；Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK；Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA；Institute of Medical Technology, Peking University Health Science Center, Beijing, China；Hangzhou Genlight MedTech Co., Ltd., China；Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China；Department of Automation, Shanghai Jiao Tong University, Shanghai, China；Department of Artificial Intelligence, Sungkyunkwan University, Seoul, South Korea；Department of Electrical；Computer Engineering, Sungkyunkwan University, Seoul, South Korea；Shanghai MediWorks Precision Instruments Co., Ltd., China；Institute of Informatics, HES-SO Valais-Wallis, Switzerland；Department of Measurement；Electronics, AGH University of Krakow, Poland；Laboratoire de Thermique et Energie de Nantes (LTeN), Université Nantes, Polytech’Nantes, Nantes, France；Research Institute of Computer Vision；Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA；Physense, BCN-Medtech, Department of Communication；Information Technologies, Universitat Pompeu Fabra, Barcelona, Spain；Department of Mathematical Modeling；Machine Learning, University of Zurich, Zurich, Switzerland；Laboratory of Brain Atlas；Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China；School of Computer；Information Engineering, Xiamen University of Technology, Xiamen, China；Vascular Research Center, University at Buffalo, NY, USA；Department of Pathology；Anatomical Sciences, University at Buffalo, NY, USA；Department of Neurosurgery, University at Buffalo, NY, USA；LPIXEL Inc., Tokyo, Japan
Hessian-Based Lightweight Neural Network HessNet for State-of-the-Art Brain Vessel Segmentation on a Minimal Training Dataset
Institute of Artificial Intelligence, M.V.Lomonosov Moscow State University（人工智能研究所，莫斯科国立大学）；Laboratory of Analysis of Semantics, Centre for Language and Semantics Technologies, Faculty of Computer Science, HSE University（语义分析实验室，语言与语义技术中心，计算机科学系，高等经济大学）；Neurosputnik LLC（Neurosputnik公司）；Mechanics and Mathematics Faculty, M.V.Lomonosov Moscow State University（力学与数学系，莫斯科国立大学）；International Laboratory of Algebraic Topology and Its Applications, Faculty of Computer Science, HSE University（国际拓扑学及其应用实验室，计算机科学系，高等经济大学）
