  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.
