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Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium...

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Autores principales: Sharma, Umesh C., Zhao, Kanhao, Mentkowski, Kyle, Sonkawade, Swati D., Karthikeyan, Badri, Lang, Jennifer K., Ying, Leslie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473636/
https://www.ncbi.nlm.nih.gov/pubmed/34589528
http://dx.doi.org/10.3389/fcvm.2021.726943
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author Sharma, Umesh C.
Zhao, Kanhao
Mentkowski, Kyle
Sonkawade, Swati D.
Karthikeyan, Badri
Lang, Jennifer K.
Ying, Leslie
author_facet Sharma, Umesh C.
Zhao, Kanhao
Mentkowski, Kyle
Sonkawade, Swati D.
Karthikeyan, Badri
Lang, Jennifer K.
Ying, Leslie
author_sort Sharma, Umesh C.
collection PubMed
description Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.
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spelling pubmed-84736362021-09-28 Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy Sharma, Umesh C. Zhao, Kanhao Mentkowski, Kyle Sonkawade, Swati D. Karthikeyan, Badri Lang, Jennifer K. Ying, Leslie Front Cardiovasc Med Cardiovascular Medicine Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications. Frontiers Media S.A. 2021-09-13 /pmc/articles/PMC8473636/ /pubmed/34589528 http://dx.doi.org/10.3389/fcvm.2021.726943 Text en Copyright © 2021 Sharma, Zhao, Mentkowski, Sonkawade, Karthikeyan, Lang and Ying. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Sharma, Umesh C.
Zhao, Kanhao
Mentkowski, Kyle
Sonkawade, Swati D.
Karthikeyan, Badri
Lang, Jennifer K.
Ying, Leslie
Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title_full Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title_fullStr Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title_full_unstemmed Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title_short Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy
title_sort modified gan augmentation algorithms for the mri-classification of myocardial scar tissue in ischemic cardiomyopathy
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473636/
https://www.ncbi.nlm.nih.gov/pubmed/34589528
http://dx.doi.org/10.3389/fcvm.2021.726943
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