MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/51999 |
Resumo: | Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. |
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MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI ScansScience & TechnologyMany methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersionHindawi Publishing CorporationUniversidade do MinhoMendrik, Adrienne M.Vincken, Koen L.Kuijf, Hugo J.Breeuwer, MarcelBouvy, Willem H.de Bresser, JeroenAlansary, Amirde Bruijne, MarleenCarass, AaronEl-Baz, AymanJog, AmodKatyal, RanveerKhan, Ali R.van der Lijn, FeddeMahmood, QaiserMukherjee, Ryanvan Opbroek, AnnegreetPaneri, SahilPereira, SergioPersson, MikaelRajchl, MartinSarikaya, DuyguSmedby, OrjanSilva, Carlos A.Vrooman, Henri A.Vyas, SaurabhWang, ChunliangZhao, LiangBiessels, Geert JanViergever, Max A.20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/51999eng1687-526510.1155/2015/81369626759553info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:34:08Zoai:repositorium.sdum.uminho.pt:1822/51999Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:29:44.952216Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
title |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
spellingShingle |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans Mendrik, Adrienne M. Science & Technology |
title_short |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
title_full |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
title_fullStr |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
title_full_unstemmed |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
title_sort |
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans |
author |
Mendrik, Adrienne M. |
author_facet |
Mendrik, Adrienne M. Vincken, Koen L. Kuijf, Hugo J. Breeuwer, Marcel Bouvy, Willem H. de Bresser, Jeroen Alansary, Amir de Bruijne, Marleen Carass, Aaron El-Baz, Ayman Jog, Amod Katyal, Ranveer Khan, Ali R. van der Lijn, Fedde Mahmood, Qaiser Mukherjee, Ryan van Opbroek, Annegreet Paneri, Sahil Pereira, Sergio Persson, Mikael Rajchl, Martin Sarikaya, Duygu Smedby, Orjan Silva, Carlos A. Vrooman, Henri A. Vyas, Saurabh Wang, Chunliang Zhao, Liang Biessels, Geert Jan Viergever, Max A. |
author_role |
author |
author2 |
Vincken, Koen L. Kuijf, Hugo J. Breeuwer, Marcel Bouvy, Willem H. de Bresser, Jeroen Alansary, Amir de Bruijne, Marleen Carass, Aaron El-Baz, Ayman Jog, Amod Katyal, Ranveer Khan, Ali R. van der Lijn, Fedde Mahmood, Qaiser Mukherjee, Ryan van Opbroek, Annegreet Paneri, Sahil Pereira, Sergio Persson, Mikael Rajchl, Martin Sarikaya, Duygu Smedby, Orjan Silva, Carlos A. Vrooman, Henri A. Vyas, Saurabh Wang, Chunliang Zhao, Liang Biessels, Geert Jan Viergever, Max A. |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Mendrik, Adrienne M. Vincken, Koen L. Kuijf, Hugo J. Breeuwer, Marcel Bouvy, Willem H. de Bresser, Jeroen Alansary, Amir de Bruijne, Marleen Carass, Aaron El-Baz, Ayman Jog, Amod Katyal, Ranveer Khan, Ali R. van der Lijn, Fedde Mahmood, Qaiser Mukherjee, Ryan van Opbroek, Annegreet Paneri, Sahil Pereira, Sergio Persson, Mikael Rajchl, Martin Sarikaya, Duygu Smedby, Orjan Silva, Carlos A. Vrooman, Henri A. Vyas, Saurabh Wang, Chunliang Zhao, Liang Biessels, Geert Jan Viergever, Max A. |
dc.subject.por.fl_str_mv |
Science & Technology |
topic |
Science & Technology |
description |
Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/51999 |
url |
http://hdl.handle.net/1822/51999 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1687-5265 10.1155/2015/813696 26759553 |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132798969184256 |