Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan CT images and the complexity of liver morphology make accurate automatic segmentation challenging. In this paper, we propose RA V-Net, which is an improved medical image automatic segmentation model based on U-Net. It has the following three main innovations. CofRes Module (Composite Original Feature Residual Module) is proposed. With more complex convolution layers and skip connections to make it obtain a higher level of image feature extraction capability and prevent gradient disappearance or explosion. AR Module (Attention Recovery Module) is proposed to reduce the computational effort of the model. In addition, the spatial features between the data pixels of the encoding and decoding modules are sensed by adjusting the channels and LSTM convolution. Finally, the image features are effectively retained. CA Module (Channel Attention Module) is introduced, which used to extract relevant channels with dependencies and strengthen them by matrix dot product, while weakening irrelevant channels without dependencies. The purpose of channel attention is achieved. The attention mechanism provided by LSTM convolution and CA Module are strong guarantees for the performance of the neural network. The accuracy of U-Net network: 0.9862, precision: 0.9118, DSC: 0.8547, JSC: 0.82. The evaluation metrics of RA V-Net, accuracy: 0.9968, precision: 0.9597, DSC: 0.9654, JSC: 0.9414. The most representative metric for the segmentation effect is DSC, which improves 0.1107 over U-Net, and JSC improves 0.1214.
Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
Multiscale X-ray Imaging (MXI) Lab, Department of Mechanical Engineering, University College London（多尺度X射线成像实验室，机械工程系，伦敦大学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（临床脑科学中心，爱丁堡大学）；MRC Weatherall Institute of Molecular Medicine, University of Oxford（MRC韦伯尔分子医学研究所，牛津大学）
An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI
Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Physical Sciences Platform, Sunnybrook Research Institute（阳光医疗研究学院物理科学平台）；UCL Cancer Institute, University College London（伦敦大学学院癌症研究所）；Systems and Biomedical Engineering Department, Cairo University（开罗大学系统与生物医学工程系）；Department of Surgery, University of Toronto（多伦多大学外科医学系）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学实验室医学与病理学系）；Sunnybrook Health Sciences Centre（阳光健康科学中心）；Department of Medical Imaging, University of Toronto（多伦多大学医学影像学系）
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography
Rongcheng College, Harbin University of Science and Technology（哈尔滨理工大学荣成学院）；Harbin University of Science and Technology（哈尔滨理工大学）；Hunan University of Technology and Business（湖南工商大学）
Using uncertainty estimation to reduce false positives in liver lesion detection
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）
Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization
Department of Mathematics and Statistics, Loyola University Chicago（洛约拉大学芝加哥分校数学与统计学系）；Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch（伊斯兰 Azad 大学科学与研究分校数学与计算机科学系）
本文提出将 3D U-Net 架构与 Bat Algorithm 结合，通过优化学习率和批大小等超参数实现 CT 图像中肝肿瘤的自动分割，有效提升了分割精度与鲁棒性。
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想集团有限公司）
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study
The Second Hospital of Shandong University, Shandong University（山东大学第二医院，山东大学）；Shenzhen Third People’s Hospital, the Second Affiliated Hospital of Southern University of Science and Technology（深圳市第三人民医院，南方科技大学第二附属医院）；The First Affiliated Hospital of Hainan Medical University（海南医科大学第一附属医院）；Wuhan Asia General Hospital（武汉亚洲总医院）；Shanxi Medical University（山西医科大学）；Chengdu First People’s Hospital（成都市第一人民医院）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）
Detection-aided liver lesion segmentation using deep learning
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；Eidgenössische Technische Hochschule Zürich (ETH Zürich)（苏黎世联邦理工学院）；Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想有限公司）
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Biomedical Perception & Intelligence Lab, University of South Dakota（南达科他大学生物医学感知与智能实验室）；Aalto University（阿尔托大学）；Malaviya National Institute of Technology Jaipur（马拉维亚国家理工学院斋浦尔分校）；Machine & Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）
MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI
Department of Biomedical Engineering（生物医学工程系）；Department of Radiology & Biomedical Imaging（放射学与生物医学影像系）；Department of Electrical Engineering（电气工程系）；Yale University（耶鲁大学）；Johns Hopkins University（约翰霍普金斯大学）；Johns Hopkins Medicine（约翰霍普金斯医学）
A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools
Department of Pathology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China（北京清华长庚医院病理科，临床医学学院，清华大学医学院，清华大学，北京，中国）；Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）
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 surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
The Netherlands Cancer Institute – Antoni van Leeuwenhoek（荷兰癌症研究所 – 安东尼·范·列文虎克）；National Center for Tumor Diseases (NCT), NCT/UCC Dresden（国家肿瘤疾病中心（NCT），NCT/UCC 德累斯顿）；DKFZ（德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学医学院及卡尔·古斯塔夫·卡鲁斯大学医院）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学触觉互联网与人机协同中心）；University of Twente（特文特大学）
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（维也纳医科大学生物医学影像与影像引导治疗部门）；Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck, Austria（因斯布鲁克医科大学临床流行病学、公共卫生、卫生经济学、医学统计与信息学研究所）；Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA（诺华制药公司，东汉诺威，新泽西州，美国）；Novartis Pharma AG, Basel, Switzerland（诺华制药集团，巴塞尔，瑞士）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心，维也纳医科大学，奥地利）；Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Austria（消化内科与肝病科，医学第三部，维也纳医科大学，奥地利）；Medical University of Vienna, Waehringer Guertel 18–20, 1090 Vienna, Austria（维也纳医科大学，瓦尔希林格特尔18–20号，1090维也纳，奥地利）
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Otto von Guericke University（奥托·冯·格里克大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor
China University of Petroleum（中国石油大学）；Qingdao Municipal Hospital（青岛市市立医院）；University of Chinese Academy of Sciences（中国科学院大学）
