# Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations

arXiv: https://arxiv.org/abs/2206.14847 · HTML: https://mlsanity.com/paper/2206.14847/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [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)
- [A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans](https://mlsanity.com/paper/2110.00466/) (2021)

## Built on by (most cited first, 2 of 2)

- [Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor](https://mlsanity.com/paper/2207.14700/) (2022)
- [FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy](https://mlsanity.com/paper/2609.08305/) (2026)

## Abstract (arXiv)

Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT) path of the small bowel in 3D. In this work, we propose to train a deep reinforcement learning tracker using datasets with different types of annotations. Specifically, we utilize CT scans that have only GT small bowel segmentation as well as ones with the GT path. It is enabled by designing a unique environment that is compatible for both, including a reward definable even without the GT path. The performed experiments proved the validity of the proposed method. The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.