An Efficient Spatial-Temporal Polyp Detection Framework for Colonoscopy Video

Recent computer-aided polyp detection systems showed its effectiveness to decrease the polyp miss rate in colonoscopy operations, which is helpful to reduce colorectal cancer mortality. However, traditional polyp detection approaches suffer from the following drawbacks: low precision and sensitivity caused by the variance of polyp's appearance, and the system may not be able to detect polyps in real time due to the high computation complexity of the detection algorithms. To alleviate those problems, we introduce a real-time detection framework that incorporates spatial and temporal information extracted from colonoscopy videos. Our framework consists of the following three components: 1) we adopt Single Shot MultiBox Detector (SSD) to generate the proposal bounding boxes in each video frame. 2) Simultaneously, we compute optical flow from neighboring frames to extract temporal information and generate another group of polyp proposals with the temporal detection network. 3) At last, the final result is generated by a fusion module that connects the end of both streams. Experimental results on ETIS-LARIB dataset demonstrate that our proposed approach reaches the state-of-the-art performance on polyp localization with real-time performance.