Abstract-Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we proposed a polyp segmentation method based on the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform effective post-processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods. Colorectal cancer was the second cause of death in the U.S. in 2015 [1] and the third in 2017 [2] . Segmentation and detection of cancer in early stages of the disease will increase the chance of survival. Colonoscopy is the preferred method for analyzing inside the colon and also removing colorectal polyps. Polyps are the most important cause of colorectal cancer. Colonoscopy is an operator-based method and human mistakes and also lack of sensitivity increase the need for computer-aided methods to segment these polyps in colonoscopy videos. Segmentation of colorectal polyp is also a challenging task because of variations in shape and color intensity of polyps in colonoscopy frames. Different methods have been proposed with the aim of accurate segmentation. We categorize research work in polyp segmentation into three main approaches. The first approach belongs to those image processing based segmentations which do not use any learning methods. The second group of approaches belongs to those methods which first extract features and then use classifiers for segmentation. In the third category, we group those approaches that use convolutional neuronal networks (CNN) and perform the segmentation.
Akbari et al. REF proposed a novel polyp segmentation method that is strongly based on cascading of the network.
Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Computer Science Medicine Engineering