AI-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management
This tool addresses the need for scalable and efficient prognostic assessment in gastrointestinal cancer patients, though it is incremental as it builds on existing segmentation methods.
The researchers tackled the problem of time-consuming and costly manual analysis of abdominal tissue composition from CT scans in gastrointestinal cancer management by developing an AI-driven automated tool, achieving a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation.
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.