Liver Cirrhosis Stage Estimation from MRI with Deep Learning

• URL: http://arxiv.org/abs/2502.18225v1
• Date: Sun, 23 Feb 2025 20:50:08 GMT
• Title: Liver Cirrhosis Stage Estimation from MRI with Deep Learning
• Authors: Jun Zeng, Debesh Jha, Ertugrul Aktas, Elif Keles, Alpay Medetalibeyoglu, Matthew Antalek, Amir A. Borhani, Daniela P. Ladner, Gorkem Durak, Ulas Bagci,
• Abstract summary: We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI.<n>Our approach integrates multi-scale feature learning with sequence-specific attention mechanisms to capture subtle tissue variations across cirrhosis progression stages.<n>Our best model achieves 72.8% accuracy on T1W and 63.8% on T2W sequences, significantly outperforming traditional radiomics-based approaches.
• Score: 8.624095515251993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an end-to-end deep learning framework for automated liver cirrhosis stage estimation from multi-sequence MRI. Cirrhosis is the severe scarring (fibrosis) of the liver and a common endpoint of various chronic liver diseases. Early diagnosis is vital to prevent complications such as decompensation and cancer, which significantly decreases life expectancy. However, diagnosing cirrhosis in its early stages is challenging, and patients often present with life-threatening complications. Our approach integrates multi-scale feature learning with sequence-specific attention mechanisms to capture subtle tissue variations across cirrhosis progression stages. Using CirrMRI600+, a large-scale publicly available dataset of 628 high-resolution MRI scans from 339 patients, we demonstrate state-of-the-art performance in three-stage cirrhosis classification. Our best model achieves 72.8% accuracy on T1W and 63.8% on T2W sequences, significantly outperforming traditional radiomics-based approaches. Through extensive ablation studies, we show that our architecture effectively learns stage-specific imaging biomarkers. We establish new benchmarks for automated cirrhosis staging and provide insights for developing clinically applicable deep learning systems. The source code will be available at https://github.com/JunZengz/CirrhosisStage.

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