Imperial College London
Dr Peter Gatehouse

DrPeterGatehouse

Faculty of Medicine, National Heart & Lung Institute

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7351 8807p.gatehouse

 
 
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Location

 

MRISydney StreetRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kuang:2021,
author = {Kuang, M and Wu, Y and Alonso-Álvarez, D and Firmin, D and Keegan, J and Gatehouse, P and Yang, G},
publisher = {arXiv},
title = {Three-dimensional embedded attentive RNN (3D-EAR) segmentor for leftventricle delineation from myocardial velocity mapping},
url = {http://arxiv.org/abs/2104.13214v1},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measureglobal and regional myocardial velocities with proved reproducibility. Accurateleft ventricle delineation is a prerequisite for robust and reproduciblemyocardial velocity estimation. Conventional manual segmentation on thisdataset can be time-consuming and subjective, and an effective fully automateddelineation method is highly in demand. By leveraging recently proposed deeplearning-based semantic segmentation approaches, in this study, we propose anovel fully automated framework incorporating a 3D-UNet backbone architecturewith Embedded multichannel Attention mechanism and LSTM based Recurrent neuralnetworks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposedmethod also utilises the amalgamation of magnitude and phase images as input torealise an information fusion of this multichannel dataset and exploring thecorrelations of temporal frames via the embedded RNN. By comparing the baselinemodel of 3D-UNet and ablation studies with and without embedded attentive LSTMmodules and various loss functions, we can demonstrate that the proposed modelhas outperformed the state-of-the-art baseline models with significantimprovement.
AU  - Kuang,M
AU  - Wu,Y
AU  - Alonso-Álvarez,D
AU  - Firmin,D
AU  - Keegan,J
AU  - Gatehouse,P
AU  - Yang,G
PB  - arXiv
PY  - 2021///
TI  - Three-dimensional embedded attentive RNN (3D-EAR) segmentor for leftventricle delineation from myocardial velocity mapping
UR  - http://arxiv.org/abs/2104.13214v1
UR  - http://hdl.handle.net/10044/1/88716
ER  -
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