An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.

BACKGROUND AND OBJECTIVE: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools...

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Autores: Urru A, Nakaki A, Benkarim O, Crovetto F, Segalés L, Comte V, Hahner N, Eixarch E, Gratacos E, Crispi F, Piella G, González Ballester MA
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p22848
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=22848
Access Level:acceso abierto
Palabra clave:Atlas
Brain
Fetal
MRI
Neonatal
Pipeline
Registration
Segmentation
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spelling An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.Urru ANakaki ABenkarim OCrovetto FSegalés LComte VHahner NEixarch EGratacos ECrispi FPiella GGonzález Ballester MAAtlasBrainFetalMRINeonatalPipelineRegistrationSegmentationBACKGROUND AND OBJECTIVE: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS: In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS: Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS: These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.ELSEVIER IRELAND LTD2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=22848Computer Methods and Programs in BiomedicineISSN: 01692607ISSNe: 18727565reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p228482026-05-27T12:37:41Z
dc.title.none.fl_str_mv An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
title An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
spellingShingle An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
Urru A
Atlas
Brain
Fetal
MRI
Neonatal
Pipeline
Registration
Segmentation
title_short An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
title_full An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
title_fullStr An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
title_full_unstemmed An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
title_sort An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis.
dc.creator.none.fl_str_mv Urru A
Nakaki A
Benkarim O
Crovetto F
Segalés L
Comte V
Hahner N
Eixarch E
Gratacos E
Crispi F
Piella G
González Ballester MA
author Urru A
author_facet Urru A
Nakaki A
Benkarim O
Crovetto F
Segalés L
Comte V
Hahner N
Eixarch E
Gratacos E
Crispi F
Piella G
González Ballester MA
author_role author
author2 Nakaki A
Benkarim O
Crovetto F
Segalés L
Comte V
Hahner N
Eixarch E
Gratacos E
Crispi F
Piella G
González Ballester MA
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Atlas
Brain
Fetal
MRI
Neonatal
Pipeline
Registration
Segmentation
topic Atlas
Brain
Fetal
MRI
Neonatal
Pipeline
Registration
Segmentation
description BACKGROUND AND OBJECTIVE: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. METHODS: In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. RESULTS: Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. CONCLUSIONS: These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=22848
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=22848
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER IRELAND LTD
publisher.none.fl_str_mv ELSEVIER IRELAND LTD
dc.source.none.fl_str_mv Computer Methods and Programs in Biomedicine
ISSN: 01692607
ISSNe: 18727565
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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