Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images

• URL: http://arxiv.org/abs/2509.26061v1
• Date: Tue, 30 Sep 2025 10:35:30 GMT
• Title: Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
• Authors: Yang Zhou, Kunhao Yuan, Ye Wei, Jishizhan Chen,
• Abstract summary: Liver fibrosis represents the accumulation of excessive extracellular matrix caused by sustained hepatic injury.<n>Recent advances in artificial intelligence-based liver segmentation and fibrosis staging offer a non-invasive alternative.<n>The CARE 2025 Challenge aimed for automated methods to quantify and analyse liver fibrosis in real-world scenarios.
• Score: 4.532336128355271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Liver fibrosis represents the accumulation of excessive extracellular matrix caused by sustained hepatic injury. It disrupts normal lobular architecture and function, increasing the chances of cirrhosis and liver failure. Precise staging of fibrosis for early diagnosis and intervention is often invasive, which carries risks and complications. To address this challenge, recent advances in artificial intelligence-based liver segmentation and fibrosis staging offer a non-invasive alternative. As a result, the CARE 2025 Challenge aimed for automated methods to quantify and analyse liver fibrosis in real-world scenarios, using multi-centre, multi-modal, and multi-phase MRI data. This challenge included tasks of precise liver segmentation (LiSeg) and fibrosis staging (LiFS). In this study, we developed an automated pipeline for both tasks across all the provided MRI modalities. This pipeline integrates pseudo-labelling based on multi-modal co-registration, liver segmentation using deep neural networks, and liver fibrosis staging based on shape, textural, appearance, and directional (STAD) features derived from segmentation masks and MRI images. By solely using the released data with limited annotations, our proposed pipeline demonstrated excellent generalisability for all MRI modalities, achieving top-tier performance across all competition subtasks. This approach provides a rapid and reproducible framework for quantitative MRI-based liver fibrosis assessment, supporting early diagnosis and clinical decision-making. Code is available at https://github.com/YangForever/care2025_liver_biodreamer.

Related papers

• Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI [13.33205970973723]
  This study introduces a multi-task deep learning framework developed for liver segmentation (LiSeg) and liver fibrosis staging (LiFS) using multiparametric MRI.<n>The LiSeg phase addresses the challenge of limited annotated images by employing a semi-supervised learning model that integrates image segmentation and registration.<n>In the LiFS phase, we employed a patchbased method which allows the visualization of liver fibrosis stages based on the classification outputs.
  arXiv  Detail & Related papers  (2026-02-10T11:40:43Z)
• Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method [31.756744402295542]
  The LiQA dataset is curated to benchmark algorithms for Liver (LiSeg) and Liver Fibrosis Staging (LiFS) under complex real-world conditions.<n>We describe the challenge's top-performing methodology, which integrates a semi-supervised learning framework with external data for robust segmentation.
  arXiv  Detail & Related papers  (2025-12-08T15:44:24Z)
• Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI [6.755224757651558]
  We propose a label-efficient segmentation approach that promotes cross-modality generalization under real-world conditions.<n>Our method integrates a foundation-scale 3D segmentation backbone adapted via fine-tuning, co-training with cross pseudo supervision to leverage unlabeled volumes.<n>Our results exhibit the effectiveness of our proposed label-efficient baseline for liver segmentation in multi-phase, multi-vendor MRI.
  arXiv  Detail & Related papers  (2025-10-06T11:19:05Z)
• SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes [10.398312170809222]
  Liver Cirrhosis plays a critical role in the prognosis of chronic liver disease.<n>Existing methods underutilize the spatial details in MRI data.<n>We introduce a novel Mamba-based network, designed to model the spatial relationships within the complex anatomical structures of MRI volumes.
  arXiv  Detail & Related papers  (2025-08-17T15:52:54Z)
• impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction [75.43342771863837]
  We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy.<n>It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches.<n>Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets.
  arXiv  Detail & Related papers  (2025-08-08T10:01:16Z)
• Large Scale MRI Collection and Segmentation of Cirrhotic Liver [1.3157208364269697]
  Liver cirrhosis is the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration.<n>Cirrhotic liver analysis presents substantial challenges due to morphological alterations and heterogeneous signal characteristics.<n>We present CirrMRI600+, the first comprehensive dataset comprising 628 high-resolution abdominal MRI scans.
  arXiv  Detail & Related papers  (2024-10-06T20:24:41Z)
• Cross-Modal Causal Intervention for Medical Report Generation [107.76649943399168]
  Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance.<n> generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases.<n>We propose a two-stage framework named CrossModal Causal Representation Learning (CMCRL)<n> Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• CoRe: An Automated Pipeline for The Prediction of Liver Resection Complexity from Preoperative CT Scans [53.561797148529664]
  Tumors located in critical positions are known to complexify liver resections. CoRe is an automated medical image processing pipeline for the prediction of postoperative LR complexity.
  arXiv  Detail & Related papers  (2022-10-15T15:29:24Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers [2.055026516354464]
  This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. With Dice scores of averaged 98+-2% for liver and 81+-28% lesion segmentation on the MRI dataset and 97+-2% and 79+-25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation.
  arXiv  Detail & Related papers  (2022-01-26T14:52:23Z)
• United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI [5.857654010519764]
  We propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. The UAL first utilizes a multi-view aware encoder to extract multi-modality NCMRI information for liver tumor segmentation and detection. The proposed mechanism of coordinate sharing with padding integrates the multi-task of segmentation and detection so that it enables multi-task to perform united adversarial learning in one discriminator.
  arXiv  Detail & Related papers  (2022-01-07T18:54:07Z)
• Edge-competing Pathological Liver Vessel Segmentation with Limited Labels [61.38846803229023]
  There is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of vessels, MVI, and carcinoma grades. We propose an Edge-competing Vessel Network (EVS-Net) which contains a segmentation network and two edge segmentation discriminators.
  arXiv  Detail & Related papers  (2021-08-01T07:28:32Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion [71.87627318863612]
  We propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset.
  arXiv  Detail & Related papers  (2020-02-22T14:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.