An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images

作者：Hashimoto Shinichi^*; Ogihara Hiroyuki; Suenaga Masato; Fujita Yusuke; Terai Shuji; Hamamoto Yoshihiko; Sakaida Isao

来源：Journal of Medical Systems, 2017, 41(8): 119.

DOI：10.1007/s10916-017-0769-5

• Capsule endoscopy
• Visible area
• Supervised learning
• Self-learning

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

• 出版日期2017-8