首个600+例肝硬化MRI数据集，助力自动分割与临床分析
Large Scale MRI Collection and Segmentation of Cirrhotic Liver
- 构建628例高分辨率MRI数据集，含4万张标注切片
- 提供专家验证的肝硬化分割标签，支持多模态分析
- 适合医学影像算法、肝病研究者使用
肝硬化是慢性肝病终末阶段，以广泛纤维化和结节性再生为特征，显著增加死亡风险。磁共振成像（MRI）可无创评估，但因形态改变和信号异质性，准确分割肝硬化肝脏仍具挑战。深度学习有望自动化该任务，但受限于缺乏大规模标注数据集。本文发布首个综合性数据集CirrMRI600+，包含628例腹部高分辨率MRI扫描（310例T1加权、318例T2加权），总计近40,000张标注切片，附有专家验证的肝硬化肝脏分割标签，并包含人口统计信息、临床参数及部分组织病理学验证。同时提供11种先进深度学习模型的基准测试结果，确立性能标准。CirrMRI600+可推动肝硬化计算分析方法的发展，加速自动化肝硬化视觉分期与个性化治疗规划的实现。
原文摘要 · Abstract (English)
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accurately segmenting cirrhotic livers presents substantial challenges due to morphological alterations and heterogeneous signal characteristics. Deep learning approaches show promise for automating these tasks, but progress has been limited by the absence of large-scale, annotated datasets. Here, we present CirrMRI600+, the first comprehensive dataset comprising 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted sequences, totaling nearly 40,000 annotated slices) with expert-validated segmentation labels for cirrhotic livers. The dataset includes demographic information, clinical parameters, and histopathological validation where available. Additionally, we provide benchmark results from 11 state-of-the-art deep learning experiments to establish performance standards. CirrMRI600+ enables the development and validation of advanced computational methods for cirrhotic liver analysis, potentially accelerating progress toward automated Cirrhosis visual staging and personalized treatment planning.
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