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The ANTsX ecosystem...
The ANTsX ecosystem for quantitative biological and medical imaging
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- Tustison, Nicholas J. (author)
- University of California, Irvine,University of Virginia
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- Cook, Philip A. (author)
- University of Pennsylvania
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- Holbrook, Andrew J. (author)
- University of California, Los Angeles
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- Johnson, Hans J. (author)
- University of Iowa
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- Muschelli, John (author)
- Johns Hopkins Bloomberg School of Public Health
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- Devenyi, Gabriel A. (author)
- Douglas Mental Health University Institute
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- Duda, Jeffrey T. (author)
- University of Pennsylvania
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- Das, Sandhitsu R. (author)
- University of Pennsylvania
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- Cullen, Nicholas C. (author)
- Lund University,Lunds universitet,Klinisk minnesforskning,Forskargrupper vid Lunds universitet,Clinical Memory Research,Lund University Research Groups
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- Gillen, Daniel L. (author)
- University of California, Irvine
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- Yassa, Michael A. (author)
- University of California, Irvine
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- Stone, James R. (author)
- University of Virginia
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- Gee, James C. (author)
- University of Pennsylvania
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- Avants, Brian B. (author)
- University of Virginia
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(creator_code:org_t)
- 2021-04-27
- 2021
- English 1 s.
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1, s. 9068-9068
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Abstract
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
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Tustison, Nichol ...
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Cook, Philip A.
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Holbrook, Andrew ...
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Johnson, Hans J.
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Muschelli, John
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Devenyi, Gabriel ...
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show more...
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Duda, Jeffrey T.
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Das, Sandhitsu R ...
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Cullen, Nicholas ...
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Gillen, Daniel L ...
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Yassa, Michael A ...
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Stone, James R.
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Gee, James C.
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Avants, Brian B.
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Radiology Nuclea ...
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Scientific Repor ...
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Lund University