ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Deep Small Bowel Segmentation with Cylindrical Topological Constraints.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model.Scientific reports · 2026Article
- Deep learning for automatic volumetric bowel segmentation on body CT images.European radiology · 2025Article
- Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume.International journal of computer assisted radiology and surgery · 2025Article
- Bidirectional teaching between lightweight multi-view networks for intestine segmentation from CT volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Anatomically plausible segmentations: Explicitly preserving topology through prior deformations.Medical image analysis · 2024Article
- Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.Medical physics · 2023Article
- Visual Image Annotation for Bowel Obstruction: Repeatability and Agreement with Manual Annotation and Neural Networks.Journal of digital imaging · 2023Article
- Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- GRAPH-BASED SMALL BOWEL PATH TRACKING WITH CYLINDRICAL CONSTRAINTS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2021Article
- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
- A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.