Abstract
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.
Abstract (translated)
磁控胶囊内窥镜(MCCE)是一种用于诊断胃疾病的新兴工具,具有舒适、安全和不需要麻醉的优势。在本文中,我们开发了算法以利用MCCE获取的视频序列来检测和测量人类的胃肠道蠕动(收缩波)。我们开发了一种空间-时间深度学习算法来检测胃肠道蠕动波并测量人类的胃肠道蠕动时间。MCCE视频序列的质量易于相机运动。我们设计了相机运动检测器(CMD)来处理MCCE视频序列,在MCCE检查期间减缓相机运动。据我们所知,我们是第一位提出基于计算机视觉的解决方案来检测和测量人类胃肠道蠕动的。我们的方法在评估胃肠道蠕动方面具有巨大的帮助诊断胃疾病的潜力。
URL
https://arxiv.org/abs/2301.10218
