Deep Small Bowel Segmentation with Cylindrical Topological Constraints

• Published in: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 12264; p. 207
• Main Authors: Shin, Seung Yeon, Lee, Sungwon, Elton, Daniel, Gulley, James L, Summers, Ronald M
• Format: Journal Article
• Language: English
• Published: Germany 01.10.2020
• Subjects: abdominal computed tomography; topological constraint; inner cylinder; Small bowel segmentation; persistent homology
• DOI: 10.1007/978-3-030-59719-1_21

We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could break the applied constraint, we propose to augment a network with an additional branch to predict an inner cylinder of the small bowel. Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation. For strict evaluation, we achieved an abdominal computed tomography dataset with dense segmentation ground-truths. The proposed method showed clear improvements in terms of four different metrics compared to the baseline method, and also showed the statistical significance from a paired t-test.