arXiv：cs.AI· Deshan Kalupahana, Sonit Singh, Praveen Ravindran, Arcot Sowmya·· 4 小时前AI 评分27
保留解剖连续性：3D 腹部 CT 结肠分割的三阶段流水线
Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans
AI 导读
该研究提出一种三阶段拓扑保持分割流水线，用于解决深度学习在 3D 腹部 CT 结肠分割中因解剖结构复杂而产生的不连通预测问题。第一阶段进行深度学习初始分割，随后通过中心线桥接重新连接不连通区域，并由重建阶段优化连续性。在 TotalSegmentator 和 RAOS 数据集上，基于重叠、距离和拓扑指标的评估显示，该方法在保持分割精度的同时提升了结构一致性。
正文
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.
| Comments: | 5 pages, 2 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.03467 [cs.CV] |
| (or arXiv:2610.03467v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03467 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | IEEE 23rd International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2026 |
| Related DOI: | https://doi.org/10.1109/ISBI61048.2026.11515927
DOI(s) linking to related resources |
Submission history
From: Deshan Kalupahana [view email]
[v1]
Fri, 2 Oct 2026 15:41:03 UTC (1,120 KB)
来源：arXiv：cs.AI · arxiv.org