Automated medical image segmentation is an essential task to aid/speed up diagnosis and treatment procedures in clinical practices. Deep convolutional neural networks have exhibited promising performance in accurate and automatic seminal segmentation. For segmentation tasks, these methods normally rely on minimizing a cost/loss function that is designed to maximize the overlap between the estimated target and the ground-truth mask delineated by the experts. A simple loss function based on the degrees of overlap (i.e., Dice metric) would not take into account the underlying shape and morphology of the target subject, as well as its realistic/natural variations; therefore, suboptimal segmentation results would be observed in the form of islands of voxels, holes, and unrealistic shapes or deformations. In this light, many studies have been conducted to refine/post-process the segmentation outcome and consider an initial guess as prior knowledge to avoid outliers and/or unrealistic estimations. In this study, a novel shape-based cost function is proposed which encourages/constrains the network to learn/capture the underlying shape features in order to generate a valid/realistic estimation of the target structure. To this end, the Principal Component Analysis (PCA) was performed on a vectorized training dataset to extract eigenvalues and eigenvectors of the target subjects. The key idea was to use the reconstruction weights to discriminate valid outcomes from outliers/erroneous estimations.
A generalizable 3D framework and model for self-supervised learning in medical imaging
Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Institute for Aerospace Studies, University of Toronto（多伦多大学航空航天研究所）；Physical Sciences Platform, Sunnybrook Research Institute（圣母医院研究学院物理科学平台）；Vector Institute, Toronto（多伦多向量研究所）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学实验室医学与病理学系）；Hurvitz Brain Sciences, Sunnybrook Health Sciences Centre（圣母医院健康科学中心Hurvitz脑科学）；Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre（圣母医院健康科学中心Harquail神经调制中心）
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
Imperial College London（帝国理工学院）；Universitat Pompeu Fabra（庞培法布拉大学）；Galgo Medical S.L.（Galgo医疗有限公司）；Nara Institute of Science and Technology (NAIST)（奈良先端科学技术大学院大学（NAIST））；Institució Catalana de Recerca i Estudis Avancats (ICREA)（加泰罗尼亚研究与高级研究机构（ICREA））；Children’s National Health System（儿童国家卫生系统）；George Washington University（乔治华盛顿大学）
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
The University of Hong Kong（香港大学）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）深圳大数据研究院）；Longgang Central Hospital of Shenzhen（深圳龙岗中心医院）；Longgang People Hospital of Shenzhen（深圳龙岗人民医院）
End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France Department of Diagnostic Radiology, McGill University, Montreal, Canada Augmented Intelligence \& Precision Health Laboratory (AIPHL), McGill University Health Centre Research Institute, Montreal, Canada
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；Hangzhou Innovation Institute of Beihang University（北京航空航天大学杭州创新研究院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Technology Sydney（悉尼科技大学）
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis
School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；College of Mathematical Sciences, BoHai University（渤海大学数学科学学院）；Australian AI Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；School of Electronic Science and Technology, Hainan University（海南大学电子科学与技术学院）
Large-Kernel Attention for 3D Medical Image Segmentation
Imperial College London（帝国理工学院）；TECNALIA, Basque Research & Technology Alliance（巴斯克研究与技术联盟TECNALIA）；University of the Basque Country（巴斯克大学）；Royal Brompton Hospital（皇家布朗普顿医院）
An Effective UNet Using Feature Interaction and Fusion for Organ Segmentation in Medical Image
National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University（合成视觉基础科学国家重点实验室，计算机科学学院，四川大学）；College of Computer Science, Sichuan University（计算机科学学院，四川大学）；College of Software Engineering, Sichuan University（软件工程学院，四川大学）
本文提出含通道空间交互、通道注意力和多级融合三模块的 U 形网络，以增强预训练特征利用，并在多器官与心脏分割基准上取得最优精度且计算开销适中。
CENet: Context Enhancement Network for Medical Image Segmentation
Faculty of Informatics and Data Science, University of Regensburg（信息学院与数据科学学院，雷根施塔特大学）；Department of Industrial and Systems Engineering, Tarbiat Modares University（工业与系统工程系，塔里克特莫达res大学）；Department of Radiology & Department of Medicine, University of British Columbia（放射科与医学系，不列颠哥伦比亚大学）；Fraunhofer Institute for Digital Medicine MEVIS（数字医学研究所MEVIS）
Cross-Domain Federated Learning in Medical Imaging
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Center（西德尼·金梅尔综合中心）
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Zhejiang University（浙江大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Ritsumeikan University（立命馆大学）；University of Science and Technology of China（中国科学技术大学）
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
Northwestern University（西北大学）；IIIT Delhi（德里印度信息技术学院）；NSIT Delhi（德里国家信息技术学院）；Space Research Institute (IWF) of Austrian Academy of Sciences（奥地利科学院空间研究所）
School of Public health, Peking University（北京大学公共卫生学院）；School of Physics, Peking University（北京大学物理学院）；School of Computer Science, Peking University（北京大学计算机学院）
TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model
Sun Yat-sen University（中山大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家肿瘤临床医学研究中心/肿瘤医院&深圳医院）；University of Macau（澳门大学）
