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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep Small Bowel Segmentation with Cylindrical Topological Constraints.

Seung Yeon Shin¹, Sungwon Lee¹, Daniel Elton¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|May 17, 2022

Summary

This summary is machine-generated.

We developed a new small bowel segmentation method using a cylindrical topological constraint. This approach improves accuracy by predicting an inner cylinder to maintain topological correctness, outperforming baseline methods.

Keywords:

Small bowel segmentation abdominal computed tomography inner cylinder persistent homology topological constraint

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Area of Science:

Medical Imaging
Computational Topology
Radiology

Background:

Accurate small bowel segmentation is crucial for diagnosing and monitoring gastrointestinal diseases.
Existing segmentation methods often struggle with topological correctness, especially when small bowel segments touch.
Persistent homology offers a robust framework for analyzing topological features.

Purpose of the Study:

To introduce a novel small bowel segmentation method incorporating a cylindrical topological constraint.
To address the challenge of touching bowel segments that can violate topological constraints.
To enhance segmentation accuracy and topological correctness using a deep learning approach.

Main Methods:

Applied a cylindrical topological constraint based on persistent homology for small bowel segmentation.
Augmented a deep learning network with an additional branch to predict an inner cylinder.
Utilized a dense ground-truth abdominal computed tomography dataset for rigorous evaluation.

Main Results:

The proposed method demonstrated significant improvements across four evaluation metrics compared to the baseline.
Statistical analysis confirmed the significance of the observed improvements via a paired t-test.
The inner cylinder prediction effectively resolved issues caused by touching bowel segments.

Conclusions:

The novel method successfully integrates cylindrical topological constraints for accurate small bowel segmentation.
Predicting an inner cylinder is an effective strategy to maintain topological correctness in challenging cases.
This approach offers a promising advancement for medical image analysis in gastroenterology.