用半监督学习实现肝脏分割与纤维化分期，提升多模态影像诊断精度。
Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI
- 融合图像分割与配准的半监督模型，利用有标签和无标签数据
- 在三通道和七通道MRI上均达90%以上分割准确率，OOD泛化能力强
- 适合医学影像分析、肝病诊断研究者参考
肝纤维化在临床中面临重大挑战，亟需精确的肝脏分割与疾病分期。基于CARE Liver 2025 Track 4挑战赛，本研究提出一个联合肝脏分割（LiSeg）与纤维化分期（LiFS）的多任务深度学习框架，使用多参数MRI数据。LiSeg阶段采用半监督学习模型，结合图像分割与配准技术，有效应对标注数据少、多模态数据复杂及域偏移问题；通过利用有标签和无标签数据，提升了模型鲁棒性。LiFS阶段采用基于图像块的方法，可可视化肝纤维化分期结果。该方法在独立测试集（包含分布内和分布外样本）上进行了验证，使用三通道MRI（T1、T2、DWI）和七通道MRI（T1、T2、DWI、GED1–GED4），表现优异。代码已开源。
原文摘要 · Abstract (English)
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
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