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Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans

2026-10-02 15:41:03
Deshan Kalupahana, Sonit Singh, Praveen Ravindran, Arcot Sowmya
arXiv_AI
             
Abstract

Accurate colon segmentation from CT images is essential for colorectal disease analysis, yet deep learning based methods often produce disconnected predictions due to complex anatomy. This study introduces a three-stage, topology-preserving segmentation pipeline to address this issue. The first stage performs initial deep learning-based segmentation, followed by centreline bridging to reconnect disjoint regions and a reconstruction stage to refine continuity. Evaluations on TotalSegmentator and RAOS datasets using overlap, distance and topology-based metrics demonstrate improved structural consistency while maintaining segmentation accuracy. The proposed method enhances topological integrity, enabling more reliable colon segmentation for clinical and research applications.

Abstract (translated)

URL

https://arxiv.org/abs/2610.03467

PDF

https://arxiv.org/pdf/2610.03467.pdf

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