Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI
1 Intelligent Modelling \& Analysis Group (IMA), 2 Lab for Uncertainty in Data；Decision Making (LUCID), \ of Computer Science, University of Nottingham, UK 3 Nottingham Biomedical Research Centre (BRC), \ of Medicine, University of Nottingham, UK；Intelligent Modelling \& Analysis Group (IMA), School of Computer Science, University of Nottingham, UK Lab for Uncertainty in Data；Decision Making (LUCID), School of Computer Science, University of Nottingham, UK Nottingham Biomedical Research Centre (BRC), School of Medicine, University of Nottingham, UK
Liver fibrosis poses a substantial challenge in clinical practice, emphasizing the necessity for precise liver segmentation and accurate disease staging. Based on the CARE Liver 2025 Track 4 Challenge, this study introduces a multi-task deep learning framework developed for liver segmentation (LiSeg) and liver fibrosis staging (LiFS) using multiparametric MRI. The LiSeg phase addresses the challenge of limited annotated images and the complexities of multi-parametric MRI data by employing a semi-supervised learning model that integrates image segmentation and registration. By leveraging both labeled and unlabeled data, the model overcomes the difficulties introduced by domain shifts and variations across modalities. In the LiFS phase, we employed a patchbased method which allows the visualization of liver fibrosis stages based on the classification outputs. Our approach effectively handles multimodality imaging data, limited labels, and domain shifts. The proposed method has been tested by the challenge organizer on an independent test set that includes in-distribution (ID) and out-of-distribution (OOD) cases using three-channel MRIs (T1, T2, DWI) and seven-channel MRIs (T1, T2, DWI, GED1-GED4). The code is freely available. Github link: https://github.com/mileywang3061/Care-Liver
Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发智能科学技术研究院）；Department of Radiology, Shanghai Public Health Clinical Center（上海公共卫生临床中心放射科）；Department of Computer Science, Information Technology University（信息技术大学计算机科学系）；Department of Computer Science, Lahore University of Management Sciences（拉合尔管理科学大学计算机科学系）
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）
MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging
School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Public Health Clinical Center, Fudan University（复旦大学附属公共卫生临床中心）；University of Oxford（牛津大学）；National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Institute of Medical Technology, Peking University（北京大学医学技术研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）
Rutgers University（罗格斯大学）；Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院）
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer. The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy. However, biopsy is invasive in nature and involves risk of procedural complications. Current methods to predict the fibrosis and NAS scores from noninvasive CT images rely heavily on either a large annotated dataset or transfer learning using pretrained networks. However, the availability of a large anno…
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected whic…
Computer Science Dept. Rutgers University（罗格斯大学计算机科学系）；Division of Gastroenterology and Hepatology Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院胃肠病学和肝病学分部）
Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a meth…
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China（复旦大学上海公共卫生临床中心放射科）；Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA（西北大学电气与计算机工程系）；Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China（上海柔性医疗机器人重点实验室）；School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Computer Science, University of Nottingham, Nottingham, UK（诺丁汉大学计算机科学学院）；Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院医疗机器人研究所）；College of Computer Science and Technology, Huaqiao University, Xiamen, China（华侨大学计算机科学与技术学院）；School of Electronic Information (School of Artificial Intelligence), Northwest University, Xi'an, China（西北大学电子信息学院（人工智能学院））；Department of Mechanical Engineering, University College London, London, UK（伦敦大学学院机械工程系）；Institute of Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, UK（爱丁堡大学神经科学与心血管研究学院）；CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China（中国科学院纳米科学卓越中心）；School of Control Science and Engineering, Shandong University, Jinan, China（山东大学控制科学与工程学院）；School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK（伯明翰大学工程学院）
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Vanderbilt University（范德堡大学）；PingAn Technology（平安科技）
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its…
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）；Vector Institute（矢量研究所）；University Health Network（大学健康网络）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）；Techna Institute（Techna研究所）
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
University of Tehran（德黑兰大学）；York University（约克大学）；Northeastern University（东北大学）；Antalya Bilim University（安塔利亚比尔姆大学）；Research Institute for Gastroenterology and Liver Diseases（胃肠病学和肝病学研究所）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；University of Waterloo（滑铁卢大学）
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
Ho Chi Minh University of Science（胡志明科技大学）；National Central University（国立中央大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Houston（休斯顿大学）；Northwestern University（西北大学）
Università degli Studi di Genova（热那亚大学）；IRCCS San Martino Hospital（IRCCS圣马蒂诺医院）
Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use…
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. W…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shandong University（山东大学）；Beijing University of Chinese Medicine（北京中医药大学）
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)
The Institute of Cancer Research（癌症研究所）；The Royal Marsden NHS Foundation Trust（皇家马斯登NHS基金会信托）；University Hospital Basel（巴塞尔大学医院）
