Large Scale MRI Collection and Segmentation of Cirrhotic Liver

TL;DR

This paper introduces CirrMRI600+, a large annotated MRI dataset for cirrhotic liver segmentation, and benchmarks deep learning models to advance automated analysis of cirrhosis.

Contribution

It provides the first extensive, expert-validated MRI dataset for cirrhotic liver segmentation and establishes baseline performance benchmarks for deep learning methods.

Findings

CirrMRI600+ includes 628 annotated MRI scans with nearly 40,000 slices.

Benchmark results from 11 deep learning models set performance standards.

The dataset facilitates development of automated cirrhosis assessment tools.

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.