Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

Abstract With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical in...

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Autores principales:	Kambiz Nael, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi, Bogdan Georgescu, Nirmal Janardhanan, Benjamin Odry, Mariappan Nadar, Michael Bush, Thomas J. Re, Stefan Huwer, Sonal Josan, Heinrich von Busch, Heiko Meyer, David Mendelson, Burton P. Drayer, Dorin Comaniciu, Zahi A. Fayad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1dfb4bbd8eff4505b1963e02abf093e9
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spelling	oai:doaj.org-article:1dfb4bbd8eff4505b1963e02abf093e92021-12-02T17:04:05ZAutomated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks10.1038/s41598-021-86022-72045-2322https://doaj.org/article/1dfb4bbd8eff4505b1963e02abf093e92021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86022-7https://doaj.org/toc/2045-2322Abstract With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.Kambiz NaelEli GibsonChen YangPascal CeccaldiYoungjin YooJyotipriya DasAmish DoshiBogdan GeorgescuNirmal JanardhananBenjamin OdryMariappan NadarMichael BushThomas J. ReStefan HuwerSonal JosanHeinrich von BuschHeiko MeyerDavid MendelsonBurton P. DrayerDorin ComaniciuZahi A. FayadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kambiz Nael Eli Gibson Chen Yang Pascal Ceccaldi Youngjin Yoo Jyotipriya Das Amish Doshi Bogdan Georgescu Nirmal Janardhanan Benjamin Odry Mariappan Nadar Michael Bush Thomas J. Re Stefan Huwer Sonal Josan Heinrich von Busch Heiko Meyer David Mendelson Burton P. Drayer Dorin Comaniciu Zahi A. Fayad Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
description	Abstract With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.
format	article
author	Kambiz Nael Eli Gibson Chen Yang Pascal Ceccaldi Youngjin Yoo Jyotipriya Das Amish Doshi Bogdan Georgescu Nirmal Janardhanan Benjamin Odry Mariappan Nadar Michael Bush Thomas J. Re Stefan Huwer Sonal Josan Heinrich von Busch Heiko Meyer David Mendelson Burton P. Drayer Dorin Comaniciu Zahi A. Fayad
author_facet	Kambiz Nael Eli Gibson Chen Yang Pascal Ceccaldi Youngjin Yoo Jyotipriya Das Amish Doshi Bogdan Georgescu Nirmal Janardhanan Benjamin Odry Mariappan Nadar Michael Bush Thomas J. Re Stefan Huwer Sonal Josan Heinrich von Busch Heiko Meyer David Mendelson Burton P. Drayer Dorin Comaniciu Zahi A. Fayad
author_sort	Kambiz Nael
title	Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
title_short	Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
title_full	Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
title_fullStr	Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
title_full_unstemmed	Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks
title_sort	automated detection of critical findings in multi-parametric brain mri using a system of 3d neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1dfb4bbd8eff4505b1963e02abf093e9
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Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

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