The ANTsX ecosystem for quantitative biological and medical imaging

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

doi:10.1038/s41598-021-87564-6

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The ANTsX ecosystem for quantitative biological and medical imaging

Journal article

Open access

Peer reviewed

The ANTsX ecosystem for quantitative biological and medical imaging

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, …

Scientific reports, Vol.11(1), pp.9068-9068

04/27/2021

DOI: 10.1038/s41598-021-87564-6

PMID: 33907199

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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.

Biomarkers

Computational Neuroscience

Details

Title: Subtitle: The ANTsX ecosystem for quantitative biological and medical imaging
Creators: Nicholas J Tustison - University of Virginia
Philip A Cook - University of Pennsylvania
Andrew J Holbrook - University of California, Los Angeles
Hans J Johnson - University of Iowa
John Muschelli - Johns Hopkins University
Gabriel A Devenyi - Douglas Mental Health University Institute
Jeffrey T Duda - University of Pennsylvania
Sandhitsu R Das - University of Pennsylvania
Nicholas C Cullen - Clinical Memory Research
Daniel L Gillen - University of California, Irvine
Michael A Yassa - University of California, Irvine
James R Stone - University of Virginia
James C Gee - University of Pennsylvania
Brian B Avants - University of Virginia
Resource Type: Journal article
Publication Details: Scientific reports, Vol.11(1), pp.9068-9068
DOI: 10.1038/s41598-021-87564-6
PMID: 33907199
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Publisher: Nature Publishing Group UK
Language: English
Date published: 04/27/2021
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry
Record Identifier: 9984185369602771

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