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

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2610.03467 [cs.CV]

(or arXiv:2610.03467v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.03467 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE 23rd International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2026

https://doi.org/10.1109/ISBI61048.2026.11515927 Focus to learn more DOI(s) linking to related resources

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