Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving performance levels making it of interest for clinical use. However, few existing approaches allow for jointly segmenting normal tissue and brain lesions. Developing a DNN for such joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on a task-specific hetero-modal imaging protocol. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from task-specific hetero-modal and partially annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper-bound of the risk to deal with missing imaging modalities. For each task, our approach reaches comparable performance than task-specific and fully-supervised models.
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Imperial College London（伦敦帝国学院）；Data Science Institute, Imperial College London（伦敦帝国学院数据科学研究所）；Beijing Institute of Technology（北京理工大学）；UK Dementia Research Institute（英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labels
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Research Center for Intelligent Medical Equipment and Devices (IMED), Xi’an Jiaotong University（西安交通大学智能医疗设备与器械研究中心）
Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity
Stony Brook University（石溪大学）；City University of New York at CSI（纽约市立大学斯塔滕岛学院）；Southern Medical University（南方医科大学）；University of Texas Southwestern Medical Centre（德克萨斯大学西南医学中心）；The Fourth Medical University（第四医科大学）；University of Wisconsin（威斯康星大学）
Registration-Grounded Spectral Fusion for Unregistered WLI/NBI Endoscopic Lesion Segmentation
The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification
Victoria University（维多利亚大学）；Bangaldesh University of Business and Technology（孟加拉国商业与技术大学）；Daffodil Institute of Information Technology（达芙妮信息技术学院）；American International University-Bangladesh (AIUB)（孟加拉国美国国际大学）；Multimedia University（多媒体大学）
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
College of Biomedical Engineering, Fudan University（复旦大学生物医学工程学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Lab（湖畔实验室）；First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；ByteDance Inc.（字节跳动公司）；Tsinghua University（清华大学）；Shanghai Jiaotong University（上海交通大学）
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
University of Exeter（埃克塞特大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Sorbonne Université（索邦大学）
Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
Institute of Neuropathology, University Hospital Münster（明斯特大学医院神经病理研究所）；Institute for Geoinformatics, University of Münster（明斯特大学地理信息研究所）；Faculty of Mathematics and Computer Science, University of Münster（明斯特大学数学与计算机科学学院）；Institute for Machine Learning in Medicine (Focus Area Psychiatry), University of Münster（明斯特大学医学机器学习研究所（精神病学重点领域））
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
University of Copenhagen（哥本哈根大学）；Cerebriu A/S（Cerebriu公司）；Rigshospitalet, Copenhagen, Denmark（哥本哈根大学医院）；Pioneer Centre for Artificial Intelligence, University of Copenhagen（哥本哈根大学人工智能先锋中心）
GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Data Science Institute, Vanderbilt University（范德堡大学数据科学研究所）；UT MD Anderson Cancer Center（得克萨斯大学MD安德森癌症中心）
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
University Hospital Basel（巴塞尔大学医院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar（伊斯河畔医院）；TranslaTUM - Central Institute for Translational Cancer Research（慕尼黑工业大学转化肿瘤学中央研究所（TranslaTUM））
Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Department of Imaging Neuroscience, University College London（影像神经科学系，伦敦大学学院）；Institute of Cognitive Neuroscience, University College London（认知神经科学研究所，伦敦大学学院）
SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology（生物医学工程系，孟加拉国工程与技术大学）；Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology（计算机科学与工程系，孟加拉国工程与技术大学）；Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology（电气与电子工程系，孟加拉国工程与技术大学）
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
School of Information Technology and Electrical Engineering, University of Queensland（昆士兰大学信息技术与电气工程学院）；Science and Engineering Faculty, Queensland University of Technology（昆士兰科技大学科学与工程学院）
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans
Southwest Jiaotong University（西南交通大学）；Shanghai Jiao Tong University（上海交通大学）；The Second Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第二医院）
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Guizhou Provincial People’s Hospital（贵州省人民医院）；Henan Provincial People’s Hospital（河南省人民医院）；Shenzhen University（深圳大学）
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Surrey（萨里大学）；University of Electronic Science and Technology of China（电子科技大学）；Tongji University（同济大学）
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
King Fahd University of Petroleum and Minerals（法赫德国王石油矿产大学）；Vinbrain JSC（Vinbrain股份公司）；Le Quy Don Technical University（黎贵敦技术大学）；University Medical Center at Ho Chi Minh City（胡志明市大学医疗中心）
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；National Institute of Neurological Disorders and Stroke（美国国家神经疾病与卒中研究所）；National Institutes of Health（美国国立卫生研究院）；University of Bern（伯尔尼大学）；University of Pennsylvania（宾夕法尼亚大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
