Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net.

ABSTRACT: Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools-DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-wei

SUBMITTER: Aldoj N 

PROVIDER: S-EPMC7459118 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature