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Colon Polyp Detection and Segmentation Based on Improved MRCNN

Colon polyps have a greater chance of developing into colon cancer, and colonoscopy is one of the most commonly used methods to detect colon polyps. However, the effectiveness of colonoscopy depends largely on the technical level of the physicians, and there are fewer experienced physicians. Besides, traditional artificial intelligence methods cannot obtain a unified model with good results for all patients. To solve these problems, a colon polyp detection and segmentation method based on mask regions convolutional neural network (MRCNN) with precise region of interest (PrROI) pooling is proposed in this article. The proposed method is constructed in two parts: 1) an image filter is used to filter the initial image, filtering out the low-quality image data and 2) the screened images are divided into multiple groups according to patients and sent to the improved MRCNN for training to obtain the corresponding private model of each patient. Data sets from an open challenge and a Chinese hospital are used to verify the effectiveness of the proposed method, which is able to detect polyps with an average-precision (AP) of 0.76 and segment polyps with an intersection over union (IOU) of 86.87%. The proposed method has a potential to assist endoscopists in detecting and segmenting polyps during colonoscopy.