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Mindboggling morphometry of human brains
Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstr...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322885/ https://www.ncbi.nlm.nih.gov/pubmed/28231282 http://dx.doi.org/10.1371/journal.pcbi.1005350 |
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author | Klein, Arno Ghosh, Satrajit S. Bao, Forrest S. Giard, Joachim Häme, Yrjö Stavsky, Eliezer Lee, Noah Rossa, Brian Reuter, Martin Chaibub Neto, Elias Keshavan, Anisha |
author_facet | Klein, Arno Ghosh, Satrajit S. Bao, Forrest S. Giard, Joachim Häme, Yrjö Stavsky, Eliezer Lee, Noah Rossa, Brian Reuter, Martin Chaibub Neto, Elias Keshavan, Anisha |
author_sort | Klein, Arno |
collection | PubMed |
description | Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available. |
format | Online Article Text |
id | pubmed-5322885 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53228852017-03-09 Mindboggling morphometry of human brains Klein, Arno Ghosh, Satrajit S. Bao, Forrest S. Giard, Joachim Häme, Yrjö Stavsky, Eliezer Lee, Noah Rossa, Brian Reuter, Martin Chaibub Neto, Elias Keshavan, Anisha PLoS Comput Biol Research Article Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available. Public Library of Science 2017-02-23 /pmc/articles/PMC5322885/ /pubmed/28231282 http://dx.doi.org/10.1371/journal.pcbi.1005350 Text en © 2017 Klein et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Klein, Arno Ghosh, Satrajit S. Bao, Forrest S. Giard, Joachim Häme, Yrjö Stavsky, Eliezer Lee, Noah Rossa, Brian Reuter, Martin Chaibub Neto, Elias Keshavan, Anisha Mindboggling morphometry of human brains |
title | Mindboggling morphometry of human brains |
title_full | Mindboggling morphometry of human brains |
title_fullStr | Mindboggling morphometry of human brains |
title_full_unstemmed | Mindboggling morphometry of human brains |
title_short | Mindboggling morphometry of human brains |
title_sort | mindboggling morphometry of human brains |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322885/ https://www.ncbi.nlm.nih.gov/pubmed/28231282 http://dx.doi.org/10.1371/journal.pcbi.1005350 |
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