Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment

Abstract We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically devel...

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Autores principales:	Wenyi Lin, Kyle Hasenstab, Guilherme Moura Cunha, Armin Schwartzman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8163ba569a264ccba16c94fb8f0d2882
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spelling	oai:doaj.org-article:8163ba569a264ccba16c94fb8f0d28822021-12-02T12:33:53ZComparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment10.1038/s41598-020-77264-y2045-2322https://doaj.org/article/8163ba569a264ccba16c94fb8f0d28822020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77264-yhttps://doaj.org/toc/2045-2322Abstract We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.Wenyi LinKyle HasenstabGuilherme Moura CunhaArmin SchwartzmanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenyi Lin Kyle Hasenstab Guilherme Moura Cunha Armin Schwartzman Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
description	Abstract We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.
format	article
author	Wenyi Lin Kyle Hasenstab Guilherme Moura Cunha Armin Schwartzman
author_facet	Wenyi Lin Kyle Hasenstab Guilherme Moura Cunha Armin Schwartzman
author_sort	Wenyi Lin
title	Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
title_short	Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
title_full	Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
title_fullStr	Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
title_full_unstemmed	Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
title_sort	comparison of handcrafted features and convolutional neural networks for liver mr image adequacy assessment
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8163ba569a264ccba16c94fb8f0d2882
work_keys_str_mv	AT wenyilin comparisonofhandcraftedfeaturesandconvolutionalneuralnetworksforlivermrimageadequacyassessment AT kylehasenstab comparisonofhandcraftedfeaturesandconvolutionalneuralnetworksforlivermrimageadequacyassessment AT guilhermemouracunha comparisonofhandcraftedfeaturesandconvolutionalneuralnetworksforlivermrimageadequacyassessment AT arminschwartzman comparisonofhandcraftedfeaturesandconvolutionalneuralnetworksforlivermrimageadequacyassessment
_version_	1718393869031178240

Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment

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