GPU-Accelerated Real-Time Gastrointestinal Diseases Detection

摘要

The process of finding diseases and abnormalities during live medical examinations has for a long time depended mostly on the medical personnel, with a limited amount of computer support. However, computer-based medical systems are currently emerging in domains like endoscopies of the gastrointestinal (GI) tract. In this context, we aim for a system that enables automatic analysis of endoscopy videos, where one use case is live computer-assisted endoscopy that increases disease-and abnormality-detection rates. In this paper, a system that tackles live automatic analysis of endoscopy videos is presented with a particular focus on the system's ability to perform in real time. The presented system utilizes different parts of a heterogeneous architecture and can be used for automatic analysis of high-definition colonoscopy videos (and a fully automated analysis of video from capsular endoscopy devices). We describe our implementation and report the system performance of our GPU-based processing framework. The experimental results show real-time stream processing and low resource consumption, and a detection precision and recall level at least as good as existing related work.