Three-dimensional embedded attentive RNN (3D-EAR) segmentor for left
ventricle delineation from myocardial velocity mapping
ventricle delineation from myocardial velocity mapping
File(s)
Working paper
Author(s)
Type
Working Paper
Abstract
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure
global and regional myocardial velocities with proved reproducibility. Accurate
left ventricle delineation is a prerequisite for robust and reproducible
myocardial velocity estimation. Conventional manual segmentation on this
dataset can be time-consuming and subjective, and an effective fully automated
delineation method is highly in demand. By leveraging recently proposed deep
learning-based semantic segmentation approaches, in this study, we propose a
novel fully automated framework incorporating a 3D-UNet backbone architecture
with Embedded multichannel Attention mechanism and LSTM based Recurrent neural
networks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposed
method also utilises the amalgamation of magnitude and phase images as input to
realise an information fusion of this multichannel dataset and exploring the
correlations of temporal frames via the embedded RNN. By comparing the baseline
model of 3D-UNet and ablation studies with and without embedded attentive LSTM
modules and various loss functions, we can demonstrate that the proposed model
has outperformed the state-of-the-art baseline models with significant
improvement.
global and regional myocardial velocities with proved reproducibility. Accurate
left ventricle delineation is a prerequisite for robust and reproducible
myocardial velocity estimation. Conventional manual segmentation on this
dataset can be time-consuming and subjective, and an effective fully automated
delineation method is highly in demand. By leveraging recently proposed deep
learning-based semantic segmentation approaches, in this study, we propose a
novel fully automated framework incorporating a 3D-UNet backbone architecture
with Embedded multichannel Attention mechanism and LSTM based Recurrent neural
networks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposed
method also utilises the amalgamation of magnitude and phase images as input to
realise an information fusion of this multichannel dataset and exploring the
correlations of temporal frames via the embedded RNN. By comparing the baseline
model of 3D-UNet and ablation studies with and without embedded attentive LSTM
modules and various loss functions, we can demonstrate that the proposed model
has outperformed the state-of-the-art baseline models with significant
improvement.
Date Issued
2021-04-26
Citation
2021
Publisher
arXiv
Copyright Statement
© 2021 The Author(s)
Identifier
http://arxiv.org/abs/2104.13214v1
Subjects
eess.IV
eess.IV
cs.CV
68T01
Notes
8 pages, 4 figures, Functional Imaging and Modeling of the Heart
Publication Status
Published