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

翻译：肝脏纤维化在临床实践中构成重大挑战，凸显了精确肝脏分割与准确疾病分期的必要性。本研究基于CARE Liver 2025 Track 4挑战赛，提出了一种为多参数MRI肝脏分割（LiSeg）与肝脏纤维化分期（LiFS）开发的多任务深度学习框架。LiSeg阶段通过采用整合图像分割与配准的半监督学习模型，解决了标注图像有限及多参数MRI数据复杂性的挑战。通过利用标注和未标注数据，该模型克服了模态间域偏移和变异带来的困难。在LiFS阶段，我们采用基于图像块的方法，可根据分类结果实现肝脏纤维化分期的可视化。我们的方法能有效处理多模态成像数据、有限标注及域偏移问题。所提方法已由挑战赛组织者在独立测试集上进行验证，该测试集包含分布内（ID）与分布外（OOD）病例，使用三通道MRI（T1、T2、DWI）和七通道MRI（T1、T2、DWI、GED1-GED4）。代码已开源。Github链接：https://github.com/mileywang3061/Care-Liver