MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study

Abstract Background Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotatio...

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Autores principales:	Ricardo A. Gonzales, Felicia Seemann, Jérôme Lamy, Hamid Mojibian, Dan Atar, David Erlinge, Katarina Steding-Ehrenborg, Håkan Arheden, Chenxi Hu, John A. Onofrey, Dana C. Peters, Einar Heiberg
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Left ventricular dysfunction Annotation Residual neural networks Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/51c36b8cfc70449b9be8ef86d76f3d88
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spelling	oai:doaj.org-article:51c36b8cfc70449b9be8ef86d76f3d882021-12-05T12:08:32ZMVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study10.1186/s12968-021-00824-21532-429Xhttps://doaj.org/article/51c36b8cfc70449b9be8ef86d76f3d882021-12-01T00:00:00Zhttps://doi.org/10.1186/s12968-021-00824-2https://doaj.org/toc/1532-429XAbstract Background Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. Methods The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. Results MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e’) and a MV plane tracking error of −0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of −0.15 ± 1.18 mm, respectively. Conclusion A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.Ricardo A. GonzalesFelicia SeemannJérôme LamyHamid MojibianDan AtarDavid ErlingeKatarina Steding-EhrenborgHåkan ArhedenChenxi HuJohn A. OnofreyDana C. PetersEinar HeibergBMCarticleLeft ventricular dysfunctionAnnotationResidual neural networksDiseases of the circulatory (Cardiovascular) systemRC666-701ENJournal of Cardiovascular Magnetic Resonance, Vol 23, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Left ventricular dysfunction Annotation Residual neural networks Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	Left ventricular dysfunction Annotation Residual neural networks Diseases of the circulatory (Cardiovascular) system RC666-701 Ricardo A. Gonzales Felicia Seemann Jérôme Lamy Hamid Mojibian Dan Atar David Erlinge Katarina Steding-Ehrenborg Håkan Arheden Chenxi Hu John A. Onofrey Dana C. Peters Einar Heiberg MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
description	Abstract Background Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. Methods The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. Results MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e’) and a MV plane tracking error of −0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of −0.15 ± 1.18 mm, respectively. Conclusion A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.
format	article
author	Ricardo A. Gonzales Felicia Seemann Jérôme Lamy Hamid Mojibian Dan Atar David Erlinge Katarina Steding-Ehrenborg Håkan Arheden Chenxi Hu John A. Onofrey Dana C. Peters Einar Heiberg
author_facet	Ricardo A. Gonzales Felicia Seemann Jérôme Lamy Hamid Mojibian Dan Atar David Erlinge Katarina Steding-Ehrenborg Håkan Arheden Chenxi Hu John A. Onofrey Dana C. Peters Einar Heiberg
author_sort	Ricardo A. Gonzales
title	MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
title_short	MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
title_full	MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
title_fullStr	MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
title_full_unstemmed	MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
title_sort	mvnet: automated time-resolved tracking of the mitral valve plane in cmr long-axis cine images with residual neural networks: a multi-center, multi-vendor study
publisher	BMC
publishDate	2021
url	https://doaj.org/article/51c36b8cfc70449b9be8ef86d76f3d88
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MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study

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