Large Scale MRI Collection and Segmentation of Cirrhotic Liver

作者：Debesh Jha, Onkar Susladkar, Vandan Gorade, Elif Keleş, Matthew Antalek, Deniz Seyithanoğlu, Timurhan Cebeci, Halil Ertugrul Aktas, Gulbiz Dagoglu Kartal, Sabahattin Kaymakoğlu, Şükrü Mehmet Ertürk, Yury Velichko, Daniela P. Ladner, Amir A. Borhani, Alpay Medetalibeyoğlu, Görkem Durak, Ulaş Bağcı · 发表于：Scientific Data · 年份：2025 · DOI：10.1038/s41597-025-05201-7 · 被引用次数：11 · 研究领域：Liver Disease Diagnosis and Treatment、Hepatocellular Carcinoma Treatment and Prognosis、Liver Disease and Transplantation

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.