Large Scale MRI Collection and Segmentation of Cirrhotic Liver

Debesh Jha, Onkar Kishor Susladkar,Vandan Gorade,Elif Keles,Matthew Antalek, Deniz Seyithanoglu,Timurhan Cebeci, Halil Ertugrul Aktas, Gulbiz Dagoglu Kartal, Sabahattin Kaymakoglu,Sukru Mehmet Erturk,Yuri Velichko,Daniela P Ladner,Amir A Borhani,Alpay Medetalibeyoglu,Gorkem Durak,Ulas Bagci

Computing Research Repository (CoRR)（2025）

Istanbul University

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Abstract

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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【要点】：本文介绍了CirrMRI600+，一个包含628个高分辨率腹部MRI扫描的大型数据集，以及使用该数据集对11种最先进的深度学习模型进行基准测试的结果，旨在推动肝硬化肝脏自动分割技术的发展。

【方法】：作者采用深度学习技术自动化分割肝硬化肝脏，并利用专家验证的标注数据训练和测试模型。

【实验】：实验使用了CirrMRI600+数据集，包含310个T1加权序列和318个T2加权序列，共计近40,000个标注切片，并得到了11个深度学习模型的性能基准结果。

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