Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Université de technologie de Compiègne（贡比涅技术大学）；University of Rouen Normandy（鲁昂诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）；Institut universitaire de France（法国大学研究院）
Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux
备注：
Preprint submitted to International Journal of Approximate Reasoning
发表信息：
International Journal of Approximate Reasoning, Volume 149, 2022, Pages 39-60,
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The feature extraction module uses an encoder-decoder framework to extract semantic feature vectors from 3D inputs. The evidential layer then uses prototypes in the feature space to compute a belief function at each voxel quantifying the uncertainty about the presence or absence of a lymphoma at this location. Two evidential layers are compared, based on different ways of using distances to prototypes for computing mass functions. The whole model is trained end-to-end by minimizing the Dice loss function. The proposed combination of deep feature extraction and evidential segmentation is shown to outperform the baseline UNet model as well as three other state-of-the-art models on a dataset of 173 patients.
Shanghai Jiao Tong University（上海交通大学）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第九人民医院）；Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）
Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network
University of Wisconsin（威斯康星大学）；University of Wisconsin Carbone Comprehensive Cancer Center（威斯康星大学卡本综合癌症中心）；Emory University School of Medicine（埃默里大学医学院）；Aflac Cancer and Blood Disorders Center（aflac癌症与血液疾病中心）；Roswell Park Comprehensive Cancer Center（罗斯威尔公园综合癌症中心）；University at Buffalo Jacobs School of Medicine and Biomedical Sciences（布法罗大学雅各布斯医学与生物医学科学学院）；Imaging and Radiation Oncology Core Rhode Island（罗德岛影像与放射肿瘤学核心机构）
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
University of British Columbia（英属哥伦比亚大学）；BC Cancer Research Institute（BC癌症研究所）；Microsoft AI for Good Lab（微软AI公益实验室）；Pomeranian Medical University（波美拉尼亚医科大学）；BC Children’s Hospital（BC儿童医院）；St. Mary’s Hospital（圣玛丽医院）；BC Cancer（BC癌症中心）
Medical Image Segmentation with Belief Function Theory and Deep Learning
University of Technology of Compiègne（贡比涅技术大学）；Heudiasyc Laboratory（欧洲计算机科学与自动化研究院）；University of Paris-Saclay（巴黎萨克雷大学）；University of Artois（阿图瓦大学）；ENSMM School（国立高等机械与微技术学院）；University of Rouen（鲁昂大学）
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention
Zhengzhou University of Light Industry（郑州轻工业大学）；Peking University Institute of Hematology（北京大学血液病研究所）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
AutoLugano: A Deep Learning Framework for Fully Automated Lymphoma Segmentation and Lugano Staging on FDG-PET/CT
East China Normal University（华东师范大学）；Changhai Hospital, Naval Medical University（海军军医大学长海医院）；Shanghai Fourth People’s Hospital Affiliated to Tongji University, School of Medicine（同济大学医学院附属上海市第四人民医院）；RadioDynamic Medical（瑞迪动力医疗）；Tsinghua Cross-Strait Research Institute（清华海峡研究院）
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Microsoft（微软公司）；Seoul St. Mary’s Hospital（首尔圣玛丽医院）；The Catholic University of Korea（韩国天主教大学）；BC Cancer（不列颠哥伦比亚癌症中心）
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation
Soochow University（苏州大学）；the University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；Changshu Institute of Technology（常熟理工学院）；the First Affiliated Hospital of Soochow University（苏州大学附属第一医院）
3D PETCT Tumor Lesion Segmentation via GCN Refinement
College of Medicine and Biological Information Engineering, Northeastern University（东北大学生物医学与信息工程学院）；Department of Electrical and Computer Engineering, Steven Institute of Technology（史蒂文斯理工学院电气与计算机工程系）；Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education（医学影像智能计算教育部重点实验室）
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
University Hospital Essen（埃森大学医院）；Cancer Research Center Cologne Essen（科隆埃森癌症研究中心）；University Medicine Essen（埃森大学医学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；University of Minho（米尼奥大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）
