Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification

Abstract In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where automatic classification of the slice ran...

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Autores principales:	Namgyu Ho, Yoon-Chul Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ce1f63f4a724d9c88e5394e8929c2b2
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spelling	oai:doaj.org-article:5ce1f63f4a724d9c88e5394e8929c2b22021-12-02T10:49:16ZEvaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification10.1038/s41598-021-81525-92045-2322https://doaj.org/article/5ce1f63f4a724d9c88e5394e8929c2b22021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81525-9https://doaj.org/toc/2045-2322Abstract In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where automatic classification of the slice range of interest is preferable. Standard cardiac image post-processing guidelines indicate the importance of the correct identification of a short axis slice range for accurate quantification. We investigated the feasibility of applying transfer learning of deep convolutional neural networks (CNNs) as a means to automatically classify the short axis slice range, as transfer learning is well suited to medical image data where labeled data is scarce and expensive to obtain. The short axis slice images were classified into out-of-apical, apical-to-basal, and out-of-basal, on the basis of short axis slice location in the LV. We developed a custom user interface to conveniently label image slices into one of the three categories for the generation of training data and evaluated the performance of transfer learning in nine popular deep CNNs. Evaluation with unseen test data indicated that among the CNNs the fine-tuned VGG16 produced the highest values in all evaluation categories considered and appeared to be the most appropriate choice for the cardiac slice range classification.Namgyu HoYoon-Chul KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Namgyu Ho Yoon-Chul Kim Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
description	Abstract In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where automatic classification of the slice range of interest is preferable. Standard cardiac image post-processing guidelines indicate the importance of the correct identification of a short axis slice range for accurate quantification. We investigated the feasibility of applying transfer learning of deep convolutional neural networks (CNNs) as a means to automatically classify the short axis slice range, as transfer learning is well suited to medical image data where labeled data is scarce and expensive to obtain. The short axis slice images were classified into out-of-apical, apical-to-basal, and out-of-basal, on the basis of short axis slice location in the LV. We developed a custom user interface to conveniently label image slices into one of the three categories for the generation of training data and evaluated the performance of transfer learning in nine popular deep CNNs. Evaluation with unseen test data indicated that among the CNNs the fine-tuned VGG16 produced the highest values in all evaluation categories considered and appeared to be the most appropriate choice for the cardiac slice range classification.
format	article
author	Namgyu Ho Yoon-Chul Kim
author_facet	Namgyu Ho Yoon-Chul Kim
author_sort	Namgyu Ho
title	Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
title_short	Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
title_full	Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
title_fullStr	Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
title_full_unstemmed	Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
title_sort	evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5ce1f63f4a724d9c88e5394e8929c2b2
work_keys_str_mv	AT namgyuho evaluationoftransferlearningindeepconvolutionalneuralnetworkmodelsforcardiacshortaxissliceclassification AT yoonchulkim evaluationoftransferlearningindeepconvolutionalneuralnetworkmodelsforcardiacshortaxissliceclassification
_version_	1718396590948876288

Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification

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