Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quantification and Analysis) dataset, established as part of the CARE 2024 challenge. Comprising $440$ patients with multi-phase, multi-center MRI scans, the dataset is curated to benchmark algorithms for Liver Segmentation (LiSeg) and Liver Fibrosis Staging (LiFS) under complex real-world conditions, including domain shifts, missing modalities, and spatial misalignment. We further describe the challenge's top-performing methodology, which integrates a semi-supervised learning framework with external data for robust segmentation, and utilizes a multi-view consensus approach with Class Activation Map (CAM)-based regularization for staging. Evaluation of this baseline demonstrates that leveraging multi-source data and anatomical constraints significantly enhances model robustness in clinical settings.

翻译：肝纤维化是全球范围内一项重大的健康负担，其准确分期对于有效的临床管理至关重要。本报告介绍了作为CARE 2024挑战赛组成部分而建立的LiQA（肝纤维化量化与分析）数据集。该数据集包含$440$名患者的多期相、多中心MRI扫描，旨在为复杂真实世界条件下（包括域偏移、模态缺失和空间错位）的肝脏分割（LiSeg）与肝纤维化分期（LiFS）算法提供基准。我们进一步描述了挑战赛中表现最佳的方法，该方法将半监督学习框架与外部数据结合以实现鲁棒分割，并采用基于类激活图（CAM）正则化的多视图共识方法进行分期。对该基准方法的评估表明，利用多源数据和解剖学约束能显著提升模型在临床环境中的鲁棒性。