Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.

ABSTRACT: Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than e

SUBMITTER: Zhu Q 

PROVIDER: S-EPMC7015773 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature