The magnetically controlled capsule endoscopy (MCCE) is an emerging modality for assessing gastrointestinal disorders due to its advantages. However, current assignments of MCCE rely on manual controlling and gastric landmarks, which are prone to omissions. We improve the scanning protocol of the MCCE in human gastric using both manual and automatic controlling methods. We design a quantitative scanning coverage ratio to measure the process of MCCE scanning within the human gastric. The proposed scanning coverage ratio is capable of guiding the manual and automatic scanning process of human gastric. Moreover, we design a deep reinforcement learning (DRL) controller for automatically navigating the capsule. Our DRL controller achieves a higher coverage ratio compared to previous research.
KEYWORDS: Electronic filtering, Video, Education and training, Cameras, Diseases and disorders, Deep learning, Tunable filters, Optical filters, Endoscopes, Algorithm development
Gastric motility disorders are caused by abnormal muscle contractions which may impede the digestive process. Traditional approaches for evaluating human gastric motility have limitations, including discomfort, use of sedation, risk of radiation exposure, and confusion in interpretation. Magnetically controlled capsule endoscopy (MCCE) provides a new way to evaluate human gastric with the advantages of comfort, safety, and no anesthesia. In this paper, we develop deep learning algorithms to detect human gastric waves captured by MCCE. We demonstrate promising experimental results both qualitatively and quantitatively. Our methods have great potential to assist in the diagnosis of human gastric disease by evaluating gastric motility.
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