# Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations Source: [Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations]() Collection index: [llms.txt]() Data updated: 2026-01-01T14:45:09.810Z Authors: Seung Yeon Shin, Ronald M. Summers Published: 2022-06-29T18:18:20.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Small Bowel Path Tracking on PathSet (2-fold cross validation)](): Small Bowel Path Tracking; PathSet (2-fold cross validation); Mean: 1050. ## Original sources and implementation [Original paper]() [PDF]()