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

Med Image Comput Comput Assist Interv. 2022 Sep;13435:549-559. doi: 10.1007/978-3-031-16443-9_53. Epub 2022 Sep 16.ABSTRACTSmall 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.PMID:37126470 | PMC:PMC10140652 | DOI:10.1007/978-3-031-16443-9_53
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Authors: Source Type: research