用深度学习自动分割腹部13区，实现无创癌症评估
Deep Learning-Based Segmentation of Peritoneal Cancer Index Regions from CT Imaging
- 基于nnU-Net和Swin UNETR模型，自动分割CT影像中的rPCI区域
- nnU-Net整体Dice达0.82，接近专家间一致性（0.88）
- 适用于临床影像评估，助力非侵入性癌症分期
腹膜转移目前通过诊断性腹腔镜评估Sugarbaker腹膜癌指数（sPCI），将腹部划分为13个区域并根据肿瘤大小评分。近期共识研究定义了3D解剖区域，推动影像学腹膜癌指数（rPCI）标准化。尽管具有临床价值，但sPCI为侵入性方法，缺乏标准化影像对应。本研究提出一种基于深度学习的CT影像rPCI区域自动分割方法。在62例经三位临床研究人员手动标注、两位专家放射科医师验证的CT扫描上，评估nnU-Net与Swin UNETR性能。采用五折交叉验证，使用Dice相似系数（Dice）、95% Hausdorff距离和平均表面距离进行评估。nnU-Net总体Dice为0.82，接近人与人之间的一致性（0.88），优于Swin UNETR（0.76），主要挑战在于右侧腹壁和小肠区域。结果证明自动化rPCI分割可行性，为非侵入性影像评估奠定基础。
原文摘要 · Abstract (English)
Peritoneal metastases are currently assessed using diagnostic laparoscopy to determine Sugarbaker's Peritoneal Cancer Index (sPCI), which works by dividing the abdomen into 13 regions and scoring each region based on tumor size. A recent consensus study defined 3D regions to facilitate a radiological PCI (rPCI), providing standardized anatomical regions for imaging-based assessment. Despite its clinical value, sPCI is invasive and lacks a standardized imaging counterpart. In this study, we propose a deep learning-based approach to automatically segment the rPCI regions on CT. We evaluate nnU-Net and Swin UNETR on 62 CT scans with rPCI regions manually annotated by three clinical researchers and validated by two expert radiologists. Performance was assessed using five-fold cross-validation with the Dice Similarity Coefficient (Dice), 95th percentile Hausdorff distance and Average Surface Distance. nnU-Net achieved an overall Dice of 0.82, approaching interobserver agreement (0.88) and outperforming Swin UNETR (0.76), with remaining challenges primarily in right flank and small-bowel regions. These results demonstrate feasibility of automated rPCI segmentation, laying the foundation for non-invasive, imaging-based assessment.
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