A graph-theoretic algorithm for small bowel path tracking in CT scans

Seung Yeon Shin; Sungwon Lee; Ronald M. Summers

doi:10.1117/12.2611878

4 April 2022 A graph-theoretic algorithm for small bowel path tracking in CT scans

Seung Yeon Shin, Sungwon Lee, Ronald M. Summers

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120333A (2022) https://doi.org/10.1117/12.2611878
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

We present a novel graph-theoretic method for small bowel path tracking. It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection. We observed that a trivial solution with many short-cuts is easily made even with the wall detection, where the tracked path penetrates indistinct walls around the contact between different parts of the small bowel. Thus, we propose to include must-pass nodes in finding the path to better cover the entire course of the small bowel. The proposed method does not entail training with ground-truth paths while the previous methods do. We acquired ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans, which enables the evaluation of the path tracking for the entire course of the small bowel. The proposed method showed clear improvements in terms of several metrics compared to the baseline method. The maximum length of the path that is tracked without an error per scan, by the proposed method, is above 800mm on average.

Conference Presentation

Citation Download Citation

Seung Yeon Shin, Sungwon Lee, and Ronald M. Summers "A graph-theoretic algorithm for small bowel path tracking in CT scans", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120333A (4 April 2022); https://doi.org/10.1117/12.2611878

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