Deep Small Bowel Segmentation with Cylindrical Topological Constraints

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12264)


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.


Small bowel segmentation Topological constraint Persistent homology Inner cylinder Abdominal computed tomography 



This research was supported by the National Institutes of Health, Clinical Center and National Cancer Institute.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical CenterNational Institutes of HealthBethesdaUSA
  2. 2.Center for Cancer Research, National Cancer InstituteNational Institutes of HealthBethesdaUSA

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