Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm

Background Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their pe...

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Autores: Valverde Valverde, Sergi, Coll, Llucia, Valencia, Liliana, Clèrigues Garcia, Albert, Oliver i Malagelada, Arnau, Vilanova, Joan Carles, Ramió i Torrentà, Lluís, Rovira, Àlex, Lladó Bardera, Xavier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/19651
Acceso en línea:http://hdl.handle.net/10256/19651
Access Level:acceso abierto
Palabra clave:Imatges -- Processament
Image processing
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatges -- Segmentació
Imaging segmentation
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spelling Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction AlgorithmValverde Valverde, SergiColl, LluciaValencia, LilianaClèrigues Garcia, AlbertOliver i Malagelada, ArnauVilanova, Joan CarlesRamió i Torrentà, LluísRovira, ÀlexLladó Bardera, XavierImatges -- ProcessamentImage processingCervell -- Imatgeria per ressonància magnèticaBrain -- Magnetic resonance imagingImatges -- SegmentacióImaging segmentationBackground Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols. Purpose To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers. Study Type Retrospective. Population Twenty-one subjects (12 women) with age range 22–48 years acquired using three different MRI scanner machines including scan/rescan in each of them. Field Strength/Sequence T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively. Assessment Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions. Statistical Tests Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods. Results The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners. Data Conclusion PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly availableThis work has been partially supported by DPI2017-86696-R from the Ministerio de Ciencia, Innovación y Universidades. Albert Clèrigues also holds a FPI grant PRE2018-083507. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this researchOpen Access funding provided thanks to the CRUE-CSIC agreement with WileyWileyMinisterio de Economía y Competitividad (Espanya)2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/19651Journal of Magnetic Resonance Imaging, 2024, vol. 59, núm. 6, p. 1991-2000Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1002/jmri.27776info:eu-repo/semantics/altIdentifier/issn/1053-1807info:eu-repo/semantics/altIdentifier/eissn/1522-2586info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-RAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/196512026-05-29T05:05:01Z
dc.title.none.fl_str_mv Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
title Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
spellingShingle Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
Valverde Valverde, Sergi
Imatges -- Processament
Image processing
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatges -- Segmentació
Imaging segmentation
title_short Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
title_full Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
title_fullStr Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
title_full_unstemmed Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
title_sort Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm
dc.creator.none.fl_str_mv Valverde Valverde, Sergi
Coll, Llucia
Valencia, Liliana
Clèrigues Garcia, Albert
Oliver i Malagelada, Arnau
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Lladó Bardera, Xavier
author Valverde Valverde, Sergi
author_facet Valverde Valverde, Sergi
Coll, Llucia
Valencia, Liliana
Clèrigues Garcia, Albert
Oliver i Malagelada, Arnau
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Lladó Bardera, Xavier
author_role author
author2 Coll, Llucia
Valencia, Liliana
Clèrigues Garcia, Albert
Oliver i Malagelada, Arnau
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Lladó Bardera, Xavier
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (Espanya)
dc.subject.none.fl_str_mv Imatges -- Processament
Image processing
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatges -- Segmentació
Imaging segmentation
topic Imatges -- Processament
Image processing
Cervell -- Imatgeria per ressonància magnètica
Brain -- Magnetic resonance imaging
Imatges -- Segmentació
Imaging segmentation
description Background Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols. Purpose To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers. Study Type Retrospective. Population Twenty-one subjects (12 women) with age range 22–48 years acquired using three different MRI scanner machines including scan/rescan in each of them. Field Strength/Sequence T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively. Assessment Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions. Statistical Tests Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods. Results The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners. Data Conclusion PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly available
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/19651
url http://hdl.handle.net/10256/19651
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/jmri.27776
info:eu-repo/semantics/altIdentifier/issn/1053-1807
info:eu-repo/semantics/altIdentifier/eissn/1522-2586
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-R
dc.rights.none.fl_str_mv Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Journal of Magnetic Resonance Imaging, 2024, vol. 59, núm. 6, p. 1991-2000
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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