构建肝纤维化量化数据集，提升医学影像分析在真实场景下的鲁棒性。
Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
- 融合多中心、多时相MRI数据，构建真实复杂环境下的评估基准。
- 半监督学习与多视角共识策略使分割和分期准确率显著提升。
- 适合医学AI研究者、临床工程师及参与医疗影像挑战赛的团队。
肝纤维化是全球重大健康负担，准确分期对临床管理至关重要。本文介绍为CARE 2024挑战赛构建的LiQA（Liver Fibrosis Quantification and Analysis）数据集，包含440例患者，涵盖多时相、多中心MRI扫描。该数据集旨在模拟真实世界复杂条件（如领域偏移、模态缺失、空间错位），用于评估肝分割（LiSeg）与肝纤维化分期（LiFS）算法性能。文中还描述了挑战赛最佳方法：结合外部数据的半监督学习框架实现鲁棒分割；采用多视角共识与基于类激活图（CAM）的正则化进行分期。评估表明，利用多源数据与解剖约束可显著提升模型在临床场景中的稳定性与泛化能力。
原文摘要 · Abstract (English)
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.
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