One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN...

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Autores: Valverde Valverde, Sergi, Salem, Mostafa, Cabezas Grebol, Mariano, Pareto, Deborah, Vilanova, Joan Carles, Ramió i Torrentà, Lluís, Rovira, Àlex, Salvi, Joaquim, Oliver i Malagelada, Arnau, Lladó Bardera, Xavier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
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:10256/16781
Acceso en línea:http://hdl.handle.net/10256/16781
Access Level:acceso abierto
Palabra clave:Esclerosi múltiple
Multiple sclerosis
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spelling One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networksValverde Valverde, SergiSalem, MostafaCabezas Grebol, MarianoPareto, DeborahVilanova, Joan CarlesRamió i Torrentà, LluísRovira, ÀlexSalvi, JoaquimOliver i Malagelada, ArnauLladó Bardera, XavierEsclerosi múltipleMultiple sclerosisIn recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labelingMariano Cabezas holds a Juan de la Cierva - Incorporación grant from the Spanish Government with reference number IJCI-2016-29240. This work has been partially supported by La Fundació la Marató de TV3, Spain; by Retos de Investigación TIN2014-55710-R, TIN2015- 73563-JIN and DPI2017-86696-R from the Ministerio de Ciencia y Tecnología, Spain. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN-X PASCAL GPU used in this researchElsevierMinisterio de Economía y Competitividad (Espanya)2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/16781http://hdl.handle.net/10256/16781NeuroImage: Clinical, 2019, vol. 21, p. 101638Articles 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/10.1016/j.nicl.2018.101638info:eu-repo/semantics/altIdentifier/issn/2213-1582info:eu-repo/grantAgreement/MINECO//TIN2014-55710-Rinfo:eu-repo/grantAgreement/MINECO//TIN2015-73563-JINinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-RAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/167812026-05-29T05:05:01Z
dc.title.none.fl_str_mv One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
title One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
spellingShingle One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
Valverde Valverde, Sergi
Esclerosi múltiple
Multiple sclerosis
title_short One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
title_full One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
title_fullStr One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
title_full_unstemmed One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
title_sort One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
dc.creator.none.fl_str_mv Valverde Valverde, Sergi
Salem, Mostafa
Cabezas Grebol, Mariano
Pareto, Deborah
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Salvi, Joaquim
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
author Valverde Valverde, Sergi
author_facet Valverde Valverde, Sergi
Salem, Mostafa
Cabezas Grebol, Mariano
Pareto, Deborah
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Salvi, Joaquim
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
author_role author
author2 Salem, Mostafa
Cabezas Grebol, Mariano
Pareto, Deborah
Vilanova, Joan Carles
Ramió i Torrentà, Lluís
Rovira, Àlex
Salvi, Joaquim
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
author2_role author
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 Esclerosi múltiple
Multiple sclerosis
topic Esclerosi múltiple
Multiple sclerosis
description In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling
publishDate 2019
dc.date.none.fl_str_mv 2019
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/10256/16781
http://hdl.handle.net/10256/16781
url http://hdl.handle.net/10256/16781
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nicl.2018.101638
info:eu-repo/semantics/altIdentifier/issn/2213-1582
info:eu-repo/grantAgreement/MINECO//TIN2014-55710-R
info:eu-repo/grantAgreement/MINECO//TIN2015-73563-JIN
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv NeuroImage: Clinical, 2019, vol. 21, p. 101638
Articles publicats (D-ATC)
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reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
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