clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Technical University of Munich（慕尼黑工业大学）；Eindhoven University of Technology（埃因霍温理工大学）
Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze
备注：
* The authors Suprosanna Shit and Johannes C. Paetzold contributed equally to the work
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.
Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
Université de Reims Champagne Ardenne（兰斯香槟阿登大学）；Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣埃蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS（CREATIS研究院）
Centerline Boundary Dice Loss for Vascular Segmentation
Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）电子与信息工程学院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）广东省航天通信与网络技术重点实验室）；International Research Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）国际人工智能研究院）
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Helmholtz Imaging（亥姆霍兹成像中心）
Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures
Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China（上海交通大学医学机器人研究所）；Department of Automation, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化系）
TopoSculpt: Betti-Steered Topological Sculpting of 3D Fine-grained Tubular Shapes
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Shanghai AI Lab（上海人工智能实验室）；Medical Image Insights（医学影像洞察）
HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；Department of Biomedical Engineering, Fudan University（复旦大学生物医学工程系）；Department of Electrical and Computer Engineering, Johns Hopkins University（约翰斯·霍普金斯大学电气与计算机工程系）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
Topology-Aware Training and Spatial Diagnostics for Fiber Bundle Segmentation in Tracer Histology
Universidad Nacional Mayor de San Marcos（圣马科斯国立大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西诺拉·A.马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Rochester School of Medicine（罗切斯特大学医学院）；McLean Hospital（麦克莱恩医院）
Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）
GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs
Center for Data Science, Peking University（北京大学数据科学中心）；Yizhun Medical AI Co., Ltd（亿准医疗人工智能有限公司）；State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
Department of Electrical Engineering, Indian Institute of Technology Ropar, India（电气工程系，印度理工学院罗帕尔学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, India（计算机科学与工程系，印度理工学院罗帕尔学院）；Artificial Intelligence and Data Engineering, Indian Institute of Technology Ropar, India（人工智能与数据工程，印度理工学院罗帕尔学院）
Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
Department of Electrical and Computer Engineering, University of California, San Diego（电气与计算机工程系，加州大学圣地亚哥分校）；Department of Ophthalmology, University of California, San Diego（眼科学系，加州大学圣地亚哥分校）
PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）
TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy
Department of Applied Mathematics and Computer Science, Technical University of Denmark（技术大学丹麦应用数学和计算机科学系）；A.I. Virtanen Institute, University of Eastern Finland（东部芬兰大学A.I. Virtanen研究所）；Department of Architecture, Faculty of Engineering, Sojo University, Japan（日本早稻田大学工学系建筑系）
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；A.I. Virtanen Institute, University of Eastern Finland（东芬兰大学人工智能维尔塔宁研究所）；Aarhus University（奥胡斯大学）
University of Waterloo（滑铁卢大学）；Vector Research Institute（矢量研究所）；Robarts Research（罗伯茨研究院）
We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow "directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves fo…
Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）；University of Peshawar（白沙瓦大学）；Macau University of Science and Technology（澳门科技大学）
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）
Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection
The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；National University of Singapore, Singapore（新加坡国立大学）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）；Nanfang Hospital, Southern Medical University, Guangzhou, China（南方医科大学附属南华医院）
Topo-R1: Detecting Topological Anomalies via Vision-Language Models
Stony Brook University（石溪大学）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Stanford University（斯坦福大学）；Penn State University（宾夕法尼亚州立大学）
Bladder Vessel Segmentation using a Hybrid Attention-Convolution Framework
Institute for System Dynamics in the University of Stuttgart（斯图加特大学系统动力学研究所）；University Hospital Tübingen（图宾根大学医院）；Department of Mechanical Engineering, Stanford University（斯坦福大学机械工程系）
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Department of Radiology, Dokuz Eylul University（多尔朱·伊勒大学放射科）；Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Institute of Natural and Applied Sciences, Dokuz Eylul University（多尔朱·伊勒大学自然科学与应用科学学院）；Department of Electrical and Electronics Engineering, Dokuz Eylul University（多尔朱·伊勒大学电气与电子工程系）；Department of Radiology, University of Wisconsin-Madison（威斯康星大学麦迪逊分校放射科）；School of Medicine, Sivas Cumhuriyet University（萨瓦斯·库尔德大学医学院）；School of Medicine, Acibadem Mehmet Ali Aydinlar University（阿克塞姆·梅赫梅特·阿里·阿迪姆大学医学院）；Department of Artificial Intelligence Engineering, Bahçeşehir University（巴切希尔大学人工智能工程系）；Faculty of Pharmacy, Sivas Cumhuriyet University（萨瓦斯·库尔德大学药学院）
VesselSim: learning 3D blood vessel segmentation without expert annotations
Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada（计算机科学与软件工程系，康科迪亚大学，蒙特利尔，加拿大）；Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada（电气与计算机工程系，康科迪亚大学，蒙特利尔，加拿大）
