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Article

Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping

by 1,2,*,†, 3,†, 4, 1,3, 2, 5, 1,3, 1,3 and 1,3,*
1
National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
2
Department of Bioengineering, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK
3
Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London SW3 6NP, UK
4
Research Computing Service, Information & Communication Technologies, Imperial College London, London SW7 2AZ, UK
5
Department of Computing, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Kyung Sung
Diagnostics 2021, 11(2), 346; https://doi.org/10.3390/diagnostics11020346
Received: 27 November 2020 / Revised: 5 February 2021 / Accepted: 17 February 2021 / Published: 19 February 2021
(This article belongs to the Special Issue Advanced Techniques in Body Magnetic Resonance Imaging)
Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data. View Full-Text
Keywords: cardiovascular; segmentation; deep learning cardiovascular; segmentation; deep learning
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MDPI and ACS Style

Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Li, B.; Gao, Y.; Firmin, D.; Keegan, J.; Yang, G. Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping. Diagnostics 2021, 11, 346. https://doi.org/10.3390/diagnostics11020346

AMA Style

Wu Y, Hatipoglu S, Alonso-Álvarez D, Gatehouse P, Li B, Gao Y, Firmin D, Keegan J, Yang G. Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping. Diagnostics. 2021; 11(2):346. https://doi.org/10.3390/diagnostics11020346

Chicago/Turabian Style

Wu, Yinzhe, Suzan Hatipoglu, Diego Alonso-Álvarez, Peter Gatehouse, Binghuan Li, Yikai Gao, David Firmin, Jennifer Keegan, and Guang Yang. 2021. "Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping" Diagnostics 11, no. 2: 346. https://doi.org/10.3390/diagnostics11020346

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