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The Insight ToolKit image registration framework

Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in...

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Autores principales: Avants, Brian B., Tustison, Nicholas J., Stauffer, Michael, Song, Gang, Wu, Baohua, Gee, James C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009425/
https://www.ncbi.nlm.nih.gov/pubmed/24817849
http://dx.doi.org/10.3389/fninf.2014.00044
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author Avants, Brian B.
Tustison, Nicholas J.
Stauffer, Michael
Song, Gang
Wu, Baohua
Gee, James C.
author_facet Avants, Brian B.
Tustison, Nicholas J.
Stauffer, Michael
Song, Gang
Wu, Baohua
Gee, James C.
author_sort Avants, Brian B.
collection PubMed
description Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in comparison to previous versions of ITK. ITK(4) supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK(4) reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK(4) contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.
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spelling pubmed-40094252014-05-09 The Insight ToolKit image registration framework Avants, Brian B. Tustison, Nicholas J. Stauffer, Michael Song, Gang Wu, Baohua Gee, James C. Front Neuroinform Neuroscience Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in comparison to previous versions of ITK. ITK(4) supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK(4) reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK(4) contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling. Frontiers Media S.A. 2014-04-28 /pmc/articles/PMC4009425/ /pubmed/24817849 http://dx.doi.org/10.3389/fninf.2014.00044 Text en Copyright © 2014 Avants, Tustison, Stauffer, Song, Wu and Gee. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Avants, Brian B.
Tustison, Nicholas J.
Stauffer, Michael
Song, Gang
Wu, Baohua
Gee, James C.
The Insight ToolKit image registration framework
title The Insight ToolKit image registration framework
title_full The Insight ToolKit image registration framework
title_fullStr The Insight ToolKit image registration framework
title_full_unstemmed The Insight ToolKit image registration framework
title_short The Insight ToolKit image registration framework
title_sort insight toolkit image registration framework
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009425/
https://www.ncbi.nlm.nih.gov/pubmed/24817849
http://dx.doi.org/10.3389/fninf.2014.00044
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