AI-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
The incidence of gastrointestinal cancers remains significantly high,
particularly in China, emphasizing the importance of accurate prognostic
assessments and effective treatment strategies. Research shows a strong
correlation between abdominal muscle and fat tissue composition and patient
outcomes. However, existing manual methods for analyzing abdominal tissue
composition are time-consuming and costly, limiting clinical research
scalability. To address these challenges, we developed an AI-driven tool for
automated analysis of abdominal CT scans to effectively identify and segment
muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view
localization model and a high-precision 2D nnUNet-based segmentation model,
demonstrating a localization accuracy of 90% and a Dice Score Coefficient of
0.967 for segmentation. Furthermore, it features an interactive interface that
allows clinicians to refine the segmentation results, ensuring high-quality
outcomes effectively. Our tool offers a standardized method for effectively
extracting critical abdominal tissues, potentially enhancing the management and
treatment for gastrointestinal cancers. The code is available at
https://github.com/NanXinyu/AI-Tool4Abdominal-Seg.git}{https://github.com/NanXinyu/AI-Tool4Abdominal-Seg.git.
