Deep learning-based segmentation of peritoneal cancer index regions from CT imaging

PURPOSE: Peritoneal metastases (PM) are staged using the surgically determined peritoneal cancer index (PCI), which requires invasive laparoscopic assessment. Although CT is routinely used for preoperative evaluation, imaging-based assessment of PM extent remains challenging and is often less structured than surgical PCI scoring. A recent consensus study defined radiological PCI (rPCI) regions for cross-sectional imaging. We present the first deep learning approach to automatically segment 13 rPCI regions on CT. METHODS: 62 contrast-enhanced CT scans were retrospectively collected across the full PCI range. Each scan was annotated into non-overlapping rPCI regions by one researcher, reviewed by a second, with disagreements resolved by a radiologist. Using fivefold cross-validation, we compared nnU-Net and Swin UNETR with Dice, 95th-percentile Hausdorff distance (HD95) and Average Surface Distance (ASD). We introduce an anatomically constrained pipeline that trains on merged super-regions and splits them during post-processing using TotalSegmentator landmarks at the hips and the ligament of Treitz. RESULTS: mm), with the largest gains in the small-bowel regions, approaching the interobserver Dice of 0.87. CONCLUSIONS: Automated rPCI region segmentation on CT is feasible and approaches interobserver agreement. Encoding anatomical boundary constraints substantially improves segmentation quality in the most challenging regions. This provides a reproducible foundation for noninvasive, imaging-based PCI assessment. The main limitations are the single-center cohort and the small interobserver subset. Code is available at: https://github.com/PieterGort/rpci-region-segmentation.