Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks.

ABSTRACT:

Background

Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images.

Methods

2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude da

SUBMITTER: Barbaroux H 

PROVIDER: S-EPMC10061808 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature