Park Junseok, Hwang Youngbae, Yoon Ju-Hong, Park Min-Gyu, Kim Jungho, Lim Yun Jeong, Chun Hoon Jai
Digestive Disease Center, Institute for Digestive Research, Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, Korea.
Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI), Seongnam, Korea.
Clin Endosc. 2019 Jul;52(4):328-333. doi: 10.5946/ce.2018.172. Epub 2019 Feb 21.
Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.
胶囊内镜检查(CE)是分析小肠疾病的首选诊断方法。然而,由于其机械限制,胶囊内镜捕获的图像数量稀少。使用计算方法进行术后管理可以提高图像质量。通过使用最近开发的计算机视觉技术,可以获得包括深度在内的额外信息。使用计算机视觉技术可以测量病变大小并跟踪胶囊内镜的轨迹,而无需额外设备。此外,CE图像的计算分析有助于在更短时间内更准确地检测病变。新引入的基于深度学习的方法比传统计算机方法显示出更显著的结果。应准备大规模标准数据集以开发优化算法,提高CE的诊断率。信息技术和医学专业人员之间需要密切合作。