A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.

<h4>Introduction</h4>Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiol...

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Autores principales:	Sophie Lancelot, Roxane Roche, Afifa Slimen, Caroline Bouillot, Elise Levigoureux, Jean-Baptiste Langlois, Luc Zimmer, Nicolas Costes
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/282dd29994844bf9acb3c406b33f0524
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spelling	oai:doaj.org-article:282dd29994844bf9acb3c406b33f05242021-11-25T05:56:07ZA multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.1932-620310.1371/journal.pone.0109113https://doaj.org/article/282dd29994844bf9acb3c406b33f05242014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0109113https://doaj.org/toc/1932-6203<h4>Introduction</h4>Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.<h4>Methods</h4>High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).<h4>Results</h4>Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.<h4>Conclusions</h4>Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.Sophie LancelotRoxane RocheAfifa SlimenCaroline BouillotElise LevigoureuxJean-Baptiste LangloisLuc ZimmerNicolas CostesPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e109113 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sophie Lancelot Roxane Roche Afifa Slimen Caroline Bouillot Elise Levigoureux Jean-Baptiste Langlois Luc Zimmer Nicolas Costes A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
description	<h4>Introduction</h4>Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.<h4>Methods</h4>High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).<h4>Results</h4>Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.<h4>Conclusions</h4>Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.
format	article
author	Sophie Lancelot Roxane Roche Afifa Slimen Caroline Bouillot Elise Levigoureux Jean-Baptiste Langlois Luc Zimmer Nicolas Costes
author_facet	Sophie Lancelot Roxane Roche Afifa Slimen Caroline Bouillot Elise Levigoureux Jean-Baptiste Langlois Luc Zimmer Nicolas Costes
author_sort	Sophie Lancelot
title	A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
title_short	A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
title_full	A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
title_fullStr	A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
title_full_unstemmed	A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.
title_sort	multi-atlas based method for automated anatomical rat brain mri segmentation and extraction of pet activity.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/282dd29994844bf9acb3c406b33f0524
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A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.

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