Detecting and measuring human gastric peristalsis using magnetically controlled capsule endoscope
This work addresses the need for non-invasive gastric motility evaluation in medical diagnostics, representing an incremental advancement in applying existing methods to a new domain.
The paper tackled the problem of detecting and measuring human gastric peristalsis using magnetically controlled capsule endoscope (MCCE) video sequences, developing a spatial-temporal deep learning algorithm and a camera motion detector to achieve this, with the result being the first computer vision-based solution for this task, potentially assisting in gastric disease diagnosis.
Magnetically controlled capsule endoscope (MCCE) is an emerging tool for the diagnosis of gastric diseases with the advantages of comfort, safety, and no anesthesia. In this paper, we develop algorithms to detect and measure human gastric peristalsis (contraction wave) using video sequences acquired by MCCE. We develop a spatial-temporal deep learning algorithm to detect gastric contraction waves and measure human gastric peristalsis periods. The quality of MCCE video sequences is prone to camera motion. We design a camera motion detector (CMD) to process the MCCE video sequences, mitigating the camera movement during MCCE examination. To the best of our knowledge, we are the first to propose computer vision-based solutions to detect and measure human gastric peristalsis. Our methods have great potential in assisting the diagnosis of gastric diseases by evaluating gastric motility.