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

arXiv: https://arxiv.org/abs/2110.00466 · HTML: https://mlsanity.com/paper/2110.00466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation](https://mlsanity.com/paper/2107.02854/) (2021)
- [Deep Small Bowel Segmentation with Cylindrical Topological Constraints](https://mlsanity.com/paper/2007.08674/) (2020)
- [Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images](https://mlsanity.com/paper/2003.01290/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations](https://mlsanity.com/paper/2206.14847/) (2022)
- [Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor](https://mlsanity.com/paper/2207.14700/) (2022)
- [Graph-Based Small Bowel Path Tracking with Cylindrical Constraints](https://mlsanity.com/paper/2207.14436/) (2022)

## Abstract (arXiv)

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.