A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans

TL;DR

This paper introduces a graph-theoretic algorithm for tracking the small bowel in CT scans, using must-pass nodes to improve path accuracy without requiring training, and demonstrates significant improvements over baseline methods.

Contribution

It proposes a novel graph-based method incorporating must-pass nodes for accurate small bowel path tracking without training on ground-truth paths.

Findings

Maximum tracked path length exceeds 800mm on average.

The method outperforms baseline in multiple metrics.

No training with ground-truth paths needed.

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.