The ANTsX ecosystem for quantitative biological and medical imaging

Abstract The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software li...

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Autores principales:	Nicholas J. Tustison, Philip A. Cook, Andrew J. Holbrook, Hans J. Johnson, John Muschelli, Gabriel A. Devenyi, Jeffrey T. Duda, Sandhitsu R. Das, Nicholas C. Cullen, Daniel L. Gillen, Michael A. Yassa, James R. Stone, James C. Gee, Brian B. Avants
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8a730c2b91564fcbbff35342a39fe8ae
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spelling	oai:doaj.org-article:8a730c2b91564fcbbff35342a39fe8ae2021-12-02T17:15:33ZThe ANTsX ecosystem for quantitative biological and medical imaging10.1038/s41598-021-87564-62045-2322https://doaj.org/article/8a730c2b91564fcbbff35342a39fe8ae2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87564-6https://doaj.org/toc/2045-2322Abstract The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.Nicholas J. TustisonPhilip A. CookAndrew J. HolbrookHans J. JohnsonJohn MuschelliGabriel A. DevenyiJeffrey T. DudaSandhitsu R. DasNicholas C. CullenDaniel L. GillenMichael A. YassaJames R. StoneJames C. GeeBrian B. AvantsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicholas J. Tustison Philip A. Cook Andrew J. Holbrook Hans J. Johnson John Muschelli Gabriel A. Devenyi Jeffrey T. Duda Sandhitsu R. Das Nicholas C. Cullen Daniel L. Gillen Michael A. Yassa James R. Stone James C. Gee Brian B. Avants The ANTsX ecosystem for quantitative biological and medical imaging
description	Abstract The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.
format	article
author	Nicholas J. Tustison Philip A. Cook Andrew J. Holbrook Hans J. Johnson John Muschelli Gabriel A. Devenyi Jeffrey T. Duda Sandhitsu R. Das Nicholas C. Cullen Daniel L. Gillen Michael A. Yassa James R. Stone James C. Gee Brian B. Avants
author_facet	Nicholas J. Tustison Philip A. Cook Andrew J. Holbrook Hans J. Johnson John Muschelli Gabriel A. Devenyi Jeffrey T. Duda Sandhitsu R. Das Nicholas C. Cullen Daniel L. Gillen Michael A. Yassa James R. Stone James C. Gee Brian B. Avants
author_sort	Nicholas J. Tustison
title	The ANTsX ecosystem for quantitative biological and medical imaging
title_short	The ANTsX ecosystem for quantitative biological and medical imaging
title_full	The ANTsX ecosystem for quantitative biological and medical imaging
title_fullStr	The ANTsX ecosystem for quantitative biological and medical imaging
title_full_unstemmed	The ANTsX ecosystem for quantitative biological and medical imaging
title_sort	antsx ecosystem for quantitative biological and medical imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8a730c2b91564fcbbff35342a39fe8ae
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The ANTsX ecosystem for quantitative biological and medical imaging

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