Medical image segmentation modeling is a high-stakes task where understanding of uncertainty is crucial for addressing visual ambiguity. Prior work has developed segmentation models utilizing probabilistic or generative mechanisms to infer uncertainty from labels where annotators draw a singular boundary. However, as these annotations cannot represent an individual annotator's uncertainty, models trained on them produce uncertainty maps that are difficult to interpret. We propose a novel segmentation representation, Confidence Contours, which uses high- and low-confidence ``contours'' to capture uncertainty directly, and develop a novel annotation system for collecting contours. We conduct an evaluation on the Lung Image Dataset Consortium (LIDC) and a synthetic dataset. From an annotation study with 30 participants, results show that Confidence Contours provide high representative capacity without considerably higher annotator effort. We also find that general-purpose segmentation models can learn Confidence Contours at the same performance level as standard singular annotations. Finally, from interviews with 5 medical experts, we find that Confidence Contour maps are more interpretable than Bayesian maps due to representation of structural uncertainty.
UCAD: Uncertainty-guided Contour-aware Displacement for semi-supervised medical image segmentation
University of Science and Technology of China（科学技术大学）；Suzhou Institute for Advanced Research（苏州先进研究院）；State Key Laboratory of Precision and Intelligent Chemistry（精密与智能化学国家重点实验室）
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
Beihang University（北京航空航天大学）；Nanyang Technological University（南洋理工大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Great Bay University（大湾区大学）；Beijing Institute of Technology（北京理工大学）
Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；National Institute of Health Data Science, Peking University, Beijing, China（北京大学国家健康数据科学研究院）；Department of Radiology, Peking University Third Hospital, Beijing, China（北京大学第三医院放射科）；RIKEN Center for Computational Science (R-CCS), Kobe, Japan（日本京都大学RIKEN计算科学中心）；National Institute of Health Data Science, Peking University, the Institute of Medical Technology, Peking University Health Science Center, and the Institute for Artificial Intelligence, Peking University, Beijing, China（北京大学国家健康数据科学研究院、北京大学医学部医学技术研究所以及北京大学人工智能研究所）
Circle Representation for Medical Instance Object Segmentation
Department of Electrical Engineering and Computer Science, Vanderbilt University（电气工程与计算机科学系，范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Department of Pathology, Vanderbilt University Medical Center（病理学系，范德堡大学医学中心）；Department of Pediatrics, Vanderbilt University Medical Center（儿科学系，范德堡大学医学中心）
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Data Science, Fudan University（复旦大学大数据学院）；School of Engineering Medicine, Beihang University（北京航空航天大学医工交叉创新研究院）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与产业研究院）；School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物与医学工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Beijing Anding Hospital, Capital Medical University（首都医科大学附属北京安定医院）；Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beihang University（北京航空航天大学生物力学与力学生物学教育部重点实验室）；Hefei Innovation Research Institute, Beihang University（北京航空航天大学合肥创新研究院）
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Moscow Gamma-Knife Center（莫斯科伽玛刀中心）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；N.N. Burdenko National Medical Research Center of Neurosurgery（N.N.布尔登科国家神经外科医学研究中心）
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
MILA (Quebec Artificial Intelligence Institute)（魁北克人工智能研究所MILA）；McGill University（麦吉尔大学）；NeuroRx Research（NeuroRx研究公司）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
Indian Institute of Technology Madras (IITM)（印度理工学院马德拉斯分校）；Healthcare Technology Innovation Centre (HTIC), IITM（印度理工学院马德拉斯分校医疗技术创新中心）；Indian Institute of Information Technology Design & Manufacturing Kancheepuram (IIITDM)（坎奇普拉姆印度信息技术设计与制造学院）；Zasti
Transformer-based Annotation Bias-aware Medical Image Segmentation
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
FSDA-DG: Improving Cross-Domain Generalizability of Medical Image Segmentation with Few Source Domain Annotations
School of Biomedical Engineering（生物医学工程学院）；College of Information Science and Technology（信息科学与技术学院）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；School of Information（信息学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（云南智能系统与计算重点实验室）；Guangdong Provincial Key Laboratory of Medical Image Processing（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology（广东省医学影像与诊断技术工程实验室）；Pazhou Lab（琶洲实验室）
Annotation-efficient deep learning for automatic medical image segmentation
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory（琶洲实验室）；Guangdong General Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院））；Renmin Hospital of Wuhan University（武汉大学人民医院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）；Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院脑认知与脑疾病研究所）；ETS Montreal（蒙特利尔高等技术学院）
Unified Medical Image Segmentation with State Space Modeling Snake
Sun Yat-sen University（中山大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Beijing University of Posts and Telecommunications（北京邮电大学）；The Australian National University（澳大利亚国立大学）
RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation
School of Engineering, College of Engineering and Physical Sciences（工程学院，工程与物理科学学院）；University of Birmingham（伯明翰大学）；Oxford Center for Clinical Magnetic Resonance Research (OCMR)（牛津临床磁共振研究所以（OCMR））；University of Oxford（牛津大学）；John Radcliffe Hospital（约翰·拉德克利夫医院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Queen Mary University London（伦敦女王学院）；Barts Health NHS Trust（巴特斯医疗信托）
Joint Object Contour Points and Semantics for Instance Segmentation
Northeastern University（东北大学）；Beijing Information Science and Technology University（北京信息科技大学）；Science and Technology on Space Physics Laboratory（空间物理重点实验室）；The University of Auckland（奥克兰大学）
目标轮廓的属性对于实例分割任务具有重要意义。然而，目前流行的大部分深度神经网络对物体边缘信息关注不多。受制作实例分割数据集时人类注释过程的启发，本文提出了Mask Point R-CNN，旨在促进神经网络对对象边界的关注。具体来说，我们创新性地将原始的人体关键点检测任务扩展到任何物体的轮廓点检测。基于这种类比，我们为Mask R-CNN提出了一个轮廓点检测辅助任务，它可以通过有效地使用特征融合策略和多任务联合训练来增强不同任务之间的梯度流。因此，模型将对对象的边缘更敏感，并且可以捕获更多的几何特征。定量地，实验结果表明，我们的方法在Cityscapes数据集上比vanilla Mask R-CNN高3.8%，在COCO数据集上高0.8%。
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University of Medicine（上海交通大学医学院附属瑞金医院）；Shanghai Jiao Tong University of Medicine（上海交通大学医学院）
