Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted MR images

Abstract

Prostate cancer is a leading cause of mortality among men. Diffusion-weighted magnetic resonance imaging (DW-MRI) has shown to be successful at monitoring and detecting prostate tumors. The clinical guidelines to interpret DW-MRI for prostate cancer requires the segmentation of the prostate gland into different zones. Moreover, computeraided detection tools which are designed to detect prostate cancer automatically, usually require the segmentation of prostate gland as a preprocessing step. In this paper, we present a segmentation algorithm for delineation of the prostate gland in DW-MRI via fully convolutional neural network. The segmentation algorithm was applied to images of 30 (testing) and 104 (training) patients and a median Dice Similarity Coefficient of 0.89 was achieved. This method is faster and returns similar results compared to registration based methods; although it has the potential to produce improved results given a larger training set.