An Innovative Polyp Detection Method from Colon Capsule Endoscopy Images Based on A Novel Combination of RCNN and DRLSE

Background: Direct detection of polyps from colon capsule endoscopy (CCE) videos is an ultimate goal not only for physicians but also for biomedical engineers who are working on automatic internal lesions like polyps. There is also a great enthusiasm among biomedical professionals to make advanced systems for aiding doctors to have a faster and accurate diagnosis by the means of polyp detection from CCE acquired video streams. Such systems must be able to localize polyps correctly and extract the whole lesions from the video frames completely.Material and Methods: In this paper, a new approach toward object-wise polyp detection from CCE frames in a video stream is proposed. The proposed method employs modified region proposal CNNs to localize the existing polyps from CCE acquired video frames and after that a level-set method known as Distance Regularized Level Set Evolution (DRLSE) is employed for automatic model-based segmentation of localized polyps. The pixel-wise detection of polyps is necessary for polyp classification and will help gastroenterologists to determine appropriate prognosis and treatment for the patients.Results and conclusion: The proposed method is trained by the means of an CCE still image dataset which includes manually annotated polyps. The trained network is then applied to CCE video images. The results demonstrate that the proposed method is able to localize and detect polyps both region-wise and pixel-wise with a good rate of accuracy.