Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

Abstract The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available...

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Autores principales:	Xavier P. Burgos-Artizzu, David Coronado-Gutiérrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, Eduard Gratacós
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/58e62354554044beb548e7ef9d97b618
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spelling	oai:doaj.org-article:58e62354554044beb548e7ef9d97b6182021-12-02T17:45:12ZEvaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes10.1038/s41598-020-67076-52045-2322https://doaj.org/article/58e62354554044beb548e7ef9d97b6182020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-67076-5https://doaj.org/toc/2045-2322Abstract The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.Xavier P. Burgos-ArtizzuDavid Coronado-GutiérrezBrenda Valenzuela-AlcarazElisenda Bonet-CarneElisenda EixarchFatima CrispiEduard GratacósNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xavier P. Burgos-Artizzu David Coronado-Gutiérrez Brenda Valenzuela-Alcaraz Elisenda Bonet-Carne Elisenda Eixarch Fatima Crispi Eduard Gratacós Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
description	Abstract The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.
format	article
author	Xavier P. Burgos-Artizzu David Coronado-Gutiérrez Brenda Valenzuela-Alcaraz Elisenda Bonet-Carne Elisenda Eixarch Fatima Crispi Eduard Gratacós
author_facet	Xavier P. Burgos-Artizzu David Coronado-Gutiérrez Brenda Valenzuela-Alcaraz Elisenda Bonet-Carne Elisenda Eixarch Fatima Crispi Eduard Gratacós
author_sort	Xavier P. Burgos-Artizzu
title	Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
title_short	Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
title_full	Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
title_fullStr	Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
title_full_unstemmed	Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
title_sort	evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/58e62354554044beb548e7ef9d97b618
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Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

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