AI自动分析腹部CT，快速精准分割肌肉脂肪组织。
AI-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management
- 融合多视角定位与2D nnUNet分割模型，实现高效识别。
- 定位准确率90%，分割Dice系数达0.967，精度高。
- 交互式界面支持医生修正，适合临床研究与诊疗使用。
胃肠道癌症发病率持续偏高，尤其在中国，精准预后评估与有效治疗策略至关重要。研究表明，腹部肌肉与脂肪组织构成与患者预后密切相关。然而，现有手动分析方法耗时且成本高，限制了临床研究的可扩展性。为此，我们开发了一款AI驱动的自动化工具，用于腹部CT扫描中肌肉、皮下脂肪和内脏脂肪的精准识别与分割。该工具结合多视角定位模型与基于nnUNet的高精度2D分割模型，在定位上达到90%准确率，分割的Dice Score Coefficient为0.967。同时，系统配备交互式界面，允许临床医生对分割结果进行优化，确保高质量输出。该工具提供标准化方法，有效提取关键腹部组织信息，有望提升胃肠道癌症的管理与治疗水平。代码已开源：https://github.com/NanXinyu/AI-Tool4Abdominal-Seg.git。
原文摘要 · Abstract (English)
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.
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