Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method

TL;DR

This paper introduces the LiQA dataset for liver fibrosis analysis, along with baseline methods demonstrating improved robustness in liver segmentation and fibrosis staging under real-world clinical conditions.

Contribution

It presents the LiQA dataset for benchmarking liver fibrosis analysis and proposes a semi-supervised, multi-view approach with CAM regularization for improved accuracy.

Findings

Multi-source data improves model robustness

Anatomical constraints enhance segmentation accuracy

CAM-based regularization benefits fibrosis staging

Abstract

Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quantification and Analysis) dataset, established as part of the CARE 2024 challenge. Comprising $440$ patients with multi-phase, multi-center MRI scans, the dataset is curated to benchmark algorithms for Liver Segmentation (LiSeg) and Liver Fibrosis Staging (LiFS) under complex real-world conditions, including domain shifts, missing modalities, and spatial misalignment. We further describe the challenge's top-performing methodology, which integrates a semi-supervised learning framework with external data for robust segmentation, and utilizes a multi-view consensus approach with Class Activation Map (CAM)-based regularization for staging. Evaluation of this baseline demonstrates that leveraging multi-source data and anatomical constraints significantly enhances model robustness in clinical settings.