Shape Recovery of Polyp Using Blood Vessel Detection and Matching Estimation by UNet In the medical diagnosis, endoscopic polyps are examined and discrimination between benign and malignant is judged via the visual observation by a medical doctor. Estimating size of the polyp is important because the larger polyp tends to become malignant if the size is more than 1 cm since it is not easy to estimate the size of the polyp except medical doctors who have several years of experience. This paper proposes reliable correspondence between two images with a slight movment of endoscope using ORB features estimating the absolute size of polyp using blood vessel information in the endoscopic images. The effectiveness of the proposed approach is confirmed via experiments.

AFPNet: Realtime AnchorFree Polyp Detection in Colonoscopy Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving stateoftheart performance with 99.36% precision and 96.44% recall.