Automatic polyp detection and localization during colonoscopy using convolutional neural networks

A computer-aided detection (CAD) second reader of colorectal polyps can decrease the rate of missed polyps in actual colonoscopy procedures. Currently, regular screening of colorectal cancer (CRC) demands a colonoscopy procedure during which polyps are located and removed. Unfortunately, different investigations have reported 22%-28% of polyps and 20%-24% of adenomatous polyps are missed. The adenoma detection rate (ADR) is a colonoscopy quality indicator highly dependent on expert training, spent time, device withdrawal technique, colon preparation and procedure-dependent factors. Several approaches have improved ADR, namely image enhancement, advancements in endoscope design and developments of accessories. Recently, artificial intelligence (AI) has shown potential to aid the task of polyp detection. This paper introduces an automatic detection of polyps that localize hyperplastic and adenomatous colorectal polyps in colonoscopy images and full video sequences. The proposed pipeline is achieved by two sequentially encoder-decoder Convolutional Neural Networks: The first detects frames with high probability of having polyps and the second estimates the actual location of the polyp. Detection of polyps showed an Annotated Area Covered AAC = 0.889 and IoU = 0.816 in actual colonoscopy images containing at least a polyp. In addition, in colonoscopy videos achieved a 0.63, 0.85, 0.65 of precision, specificity, and F1-score respectively for the ASU-Mayo database.