  Colorectal cancer is a 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 convolutional neural network. Performance of the
method is enhanced by two strategies. First, we perform a novel image patch
selection method in the training phase of the network. Second, in the test
phase, we perform an 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.
