Paper ID: 2410.16296

CirrMRI600+: 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 Ladner, Amir A. Borhani, Alpay Medetalibeyoglu, Gorkem Durak, Ulas Bagci

Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagnosis and management of end-stage cirrhosis are significant clinical challenges. Magnetic resonance imaging (MRI) is a widely available, non-invasive imaging technique for cirrhosis assessment. However, the stage of liver fibrosis cannot be easily differentiated. Moreover, the fibrotic liver tissue (cirrhotic liver) causes significant change in liver enhancement, morphology and signal characteristics, which poses substantial challenges for the development of computer-aided diagnostic applications. Deep learning (DL) offers a promising solution for automatically segmenting and recognizing cirrhotic livers in MRI scans, potentially enabling fibrosis stage classification. However, the lack of datasets specifically focused on cirrhotic livers has hindered progress. CirrMRI600+ addresses this critical gap. This extensive dataset, the first of its kind, comprises 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted, totaling nearly 40,000 slices) with annotated segmentation labels for cirrhotic livers. Unlike previous datasets, CirrMRI600+ specifically focuses on cirrhotic livers, capturing the complexities of this disease state. The link to the dataset is made publicly available at: this https URL We also share 11 baseline deep learning segmentation methods used in our rigorous benchmarking experiments: this https URL

Submitted: Oct 6, 2024