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

2026-10-02

AI Summary

This study addresses the challenge of fragmented predictions and topological discontinuities in deep learning-based 3D colon segmentation from abdominal CT scans, which frequently arise due to complex anatomical structures. To overcome this limitation, we propose a three-stage topology-preserving framework that sequentially performs initial segmentation, centerline extraction with bridging reconnection, and 3D reconstruction optimization. By explicitly introducing a centerline bridging mechanism, the method effectively repairs disconnected segments. Experimental validation on the TotalSegmentator and RAOS datasets demonstrates that the proposed approach significantly enhances the topological integrity and anatomical continuity of segmentation results. The framework outperforms existing baselines across key topological metrics, enabling more reliable clinical-grade colon segmentation.

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.

Problem

• Colon Segmentation
• 3D Abdominal CT
• Anatomical Continuity
• Topology Preservation
• Deep Learning

Innovation

• Colon Segmentation
• Topology Preservation
• Three-Stage Pipeline
• Centreline Bridging
• 3D CT