Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
The University of Sydney（悉尼大学）；Harvard Medical School（哈佛医学院）；University of Electronic Science and Technology of China（电子科技大学）；Brigham and Women’s Hospital（布莱根妇女医院）
针对无监督医学图像配准缺乏解剖知识的问题，提出融合 SAM 分割先验、原型学习与轮廓感知损失的配准框架，在多数据集上显著提升复杂结构和模糊边界场景的配准性能。
PAII Inc.（PAII公司）；University of South Carolina（南卡罗来纳大学）；University of Rochester（罗切斯特大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotations in a scalable manner. Methods that permit to produce accurate anatomical structure segmentation without using a large amount of fully annotated training images are highly desirable. In this work, we propose a novel contribution of Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism. Segmentation is formulated by learning a contour evolution beh…
The University of Hong Kong（香港大学）；Tencent（腾讯）；Huazhong University of Science and Technology（华中科技大学）
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach…
Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial
University of New South Wales（新南威尔士大学）；Ingham Institute for Applied Medical Research（英厄姆应用医学研究所）；Liverpool and Macarthur Cancer Therapy Centres（利物浦与麦克阿瑟癌症治疗中心）；Peter MacCallum Cancer Centre（彼得·麦卡勒姆癌症中心）；University of Melbourne（墨尔本大学）；CSIRO Australian e-Health Research Centre（联邦科学与工业研究组织澳大利亚电子健康研究中心）；University of Sydney（悉尼大学）；The University of Western Australia（西澳大学）；Sir Charles Gardiner Hospital（查尔斯·加德纳爵士医院）；University of Wisconsin（威斯康星大学）；Radformation Inc.（Radformation公司）
Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, China（人工智能与先进计算学院，西安交通大学利物浦大学，中国）；School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, United Kingdom（电子工程、电子科学与计算机科学学院，利物浦大学，英国）；School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom（物理科学学院，利物浦大学，英国）；School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, China（数学与物理学院，西安交通大学利物浦大学，中国）；Institute of Artificial Intelligence Innovation, Zhejiang University of Technology, China（人工智能创新研究院，浙江工业大学，中国）
FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks
The Chinese University of Hong Kong（香港中文大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Massachusetts General Brigham（麻省总医院布里格姆医疗系统）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）
Muscle volume quantification: guiding transformers with anatomical priors
Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；Technical University of Munich (TUM)（慕尼黑工业大学）；INSEP（法国国家体育学院）；Fédération Française de Badminton（法国羽毛球联合会）
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究所（AIRI））；Institute for Information Transmission Problems（信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Left ventricular segmentation is essential for measuring left ventricular function indices. Segmentation of one or several images requires an initial guess of the contour. It is hypothesized here that creating an initial guess by first detecting anatomical markers, would lead to correct detection of the endocardium. The first step of the algorithm presented here includes automatic detection of the mitral valve. Next, the apex is detected in the same frame. The valve is then tracked throughout the cardiac cycle. Contours passing from the apex to each valve corner are then found using a dynamic …
UOD: Universal One-shot Detection of Anatomical Landmarks
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，科学技术大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院智能信息处理重点实验室，计算技术研究所，中国科学院）；Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences（放射科，广东省人民医院，广东省医学科学院）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences（广东省人工智能在医学影像分析与应用重点实验室，广东省人民医院，广东省医学科学院）
本文提出 UOD 框架，通过领域自适应卷积与 transformer 与领域共享模块，在每域仅一个标注样本的条件下实现跨头、手、胸多领域 X 射线标志点检测，并达到最先进性能。
Shanghai Jiaotong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）；Sensetime Research（商汤科技研究院）
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead, respectively. Previous works usually fail to leverage the anatomical structure and suffer from class imbalance and low contrast problems. Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised…
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Computer Science, University of Maryland（马里兰大学计算机科学系）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Department of Radiation Oncology and Winship Cancer Institute, Emory University（埃默里大学放射肿瘤学系和Winship癌症研究所）
CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；Anhui Medical University（安徽医科大学）
Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China（计算机科学与技术学院，广东技术大学，广州，中国）；Neurological Institute, Cleveland Clinic, OH, USA（神经医学研究院，克利夫兰医学中心，俄亥俄州，美国）；Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China（神经外科，中山大学附属第一医院，广州，中国）；Brigham and Women’s Hospital, Harvard Medical School, MA, USA（布里奇沃特医院，哈佛医学院，马萨诸塞州，美国）；Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands（放射科，医学影像中心，格罗宁根大学医学中心，格罗宁根大学，荷兰）
The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-t…
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院，空天地海一体化大数据应用技术国家工程实验室）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Beijing United Family Hospital（北京和睦家医院）；The First Affiliated Hospital, Zhejiang University（浙江大学医学院附属第一医院）；Peking Union Medical College Hospital（北京协和医院）
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.e., the region between an artery's lumen and outer wall, in black-blood magnetic resonance (MR) images. Commonly used convolutional neural networks (CNNs) for semantic segmentation are suboptimal for this task as their use does not guarantee a contiguous ring-shaped segmentation. Instead, in this work, we cast vessel wall segmentation as a multi-task regression problem in a polar coordinate system. For each carotid a…
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Department of Radiology, Northwestern University, Chicago, IL, USA（西北大学放射科）；Data Science Institute, Vanderbilt University, Nashville, TN, USA（范德比尔特大学数据科学研究院）；Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二医院骨科）；Second Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二临床医学院）；School of Computer Science, University of Sydney, Sydney, NSW, Australia（悉尼大学计算机科学学院）
Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（香港理工大学健康科技与信息学系）；Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham（诺丁汉大学医学院诺丁汉生物医学研究中心）
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
Department of Computing, Imperial College London, UK（伦敦帝国理工学院计算机系）；Technical University Munich, DE（慕尼黑技术大学）；Munich Center for Machine Learning (MCML), DE（慕尼黑机器学习中心（MCML））
Xiamen University（厦门大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Deepwise AI Laboratory（深智云AI实验室）
Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of annotations for training to avoid overfitting and achieving promising results. In clinical practices, massive semantic annotations are difficult to acquire in some conditions where specialized biomedical expert knowledge is required, and it is also a common condition where only few annotated classes are available. In this work, we proposed a novel method for few-shot medical image segmentation, which enables a segmentatio…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）
Image contour based vision measurement is widely applied in robot manipulation and industrial automation. It is appealing to realize object-agnostic vision system, which can be conveniently reused for various types of objects. We propose the contour primitive of interest extraction network (CPieNet) based on the one-shot learning framework. First, CPieNet is featured by that its contour primitive of interest (CPI) output, a designated regular contour part lying on a specified object, provides the essential geometric information for vision measurement. Second, CPieNet has the one-shot learning …
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）
AnatomicalNets: A Multi-Structure Segmentation and Contour-Based Distance Estimation Pipeline for Clinically Grounded Lung Cancer T-Staging
Department of Robotics and Mechatronics Engineering, University of Dhaka（达卡大学机器人与机电工程系）；Department of Electrical and Electronics Engineering, University of Dhaka（达卡大学电气与电子工程系）；Department of Electrical Engineering, Qatar University（卡塔尔大学电气工程系）；Department of Radiology, Hamad Medical Corporation（哈马德医疗集团放射科）；Department of Biomedical Technology, Prince Sattam Bin Abdulaziz University（普林斯·萨塔姆·本·阿卜杜勒阿齐兹大学生物医学技术系）
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Intel Corporation（英特尔公司）；University of Copenhagen（哥本哈根大学）
Adversarial attacks consist in maliciously changing the input data to mislead the predictions of automated decision systems and are potentially a serious threat for automated medical image analysis. Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting. In this article, we studied the effectiveness of adversarial attacks in targeted modification of segmentations of anatomical structures in chest X-rays. Firstly, we experimented with using anatomically implausible shape…
Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport
School of Information Science and Technology, ShanghaiTech University, Shanghai, China（信息科学与技术学院，上海科技大学）；School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（生物医学工程学院，上海交通大学）
Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；School of Information Management and Engineering, Shanghai University of Finance and Economics（上海财经大学信息管理与工程学院）；Department of Applied Computing, Michigan Technological University（密歇根理工大学应用计算系）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）；Department of Computer Science, Kennesaw State University（肯尼索州立大学计算机科学系）
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Peking University Third Hospital（北京大学第三医院）；Beijing Children’s Hospital, Capital Medical University（首都医科大学附属北京儿童医院）；Duke University（杜克大学）
A Systematic Review of Few-Shot Learning in Medical Imaging
Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council of Italy (ISTI-CNR)（意大利国家研究委员会信息科学与技术研究所）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）
FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI
Sejong University（世宗大学）；Forschungszentrum Jülich（于利希研究中心）；Athinoula A. Martinos Center for Biomedical Imaging（Athinoula A. Martinos生物医学成像中心）；Harvard Medical School（哈佛医学院）；Hongik University（弘益大学）
CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（数据科学研究所，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands（先进计算科学系，科学与工程学院，马斯特里赫特大学，马斯特里赫特，荷兰）；VITO, Belgium（比利时VITO研究院）
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Department of Computer Science（计算机科学系）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；School of Biomedical Engineering（生物医学工程学院）；School of Artificial Intelligence（人工智能学院）；Shanghai Jiaotong University（上海交通大学）；Department of Orthopedics（骨科系）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；Yale University（耶鲁大学）
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）；Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究所）
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifyin…
