A distance map regularized CNN for cardiac cine MR image segmentation.

ABSTRACT:

Purpose

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images.

Methods

We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel-wise distance map regression. The network also predicts uncertainties as

SUBMITTER: Dangi S 

PROVIDER: S-EPMC7372294 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature