Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Cervell; Imatge per ressonància magnètica; Aprenentatge transductiu

Detalles Bibliográficos
Autores: Kushibar, Kaisar, Salem, Mostafa, Rovira Cañellas, Alex, Salvi, Joaquim, Oliver, Arnau, Valverde, Sergi, Llado, Xavier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
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:11351/6704
Acceso en línea:https://hdl.handle.net/11351/6704
http://hdl.handle.net/11351/6704
Access Level:acceso abierto
Palabra clave:Cervell - Imatgeria per ressonància magnètica
Imatgeria (Tècnica)
ANATOMY::Nervous System::Central Nervous System::Brain
Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging
ANATOMÍA::sistema nervioso::sistema nervioso central::encéfalo
Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética
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network_name_str España
repository_id_str
dc.title.none.fl_str_mv Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
title Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
spellingShingle Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
Kushibar, Kaisar
Cervell - Imatgeria per ressonància magnètica
Imatgeria (Tècnica)
ANATOMY::Nervous System::Central Nervous System::Brain
Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging
ANATOMÍA::sistema nervioso::sistema nervioso central::encéfalo
Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética
title_short Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
title_full Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
title_fullStr Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
title_full_unstemmed Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
title_sort Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation
dc.creator.none.fl_str_mv Kushibar, Kaisar
Salem, Mostafa
Rovira Cañellas, Alex
Salvi, Joaquim
Oliver, Arnau
Valverde, Sergi
Llado, Xavier
author Kushibar, Kaisar
author_facet Kushibar, Kaisar
Salem, Mostafa
Rovira Cañellas, Alex
Salvi, Joaquim
Oliver, Arnau
Valverde, Sergi
Llado, Xavier
author_role author
author2 Salem, Mostafa
Rovira Cañellas, Alex
Salvi, Joaquim
Oliver, Arnau
Valverde, Sergi
Llado, Xavier
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Institut Català de la Salut
[Kushibar K, Valverde S, Salvi J, Oliver A, Lladó X] Institute of Computer Vision and Robotics, University of Girona, Girona, Spain. [Salem M] Institute of Computer Vision and Robotics, University of Girona, Girona, Spain. Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut, Egypt. [Rovira À] Unitat de Ressonància Magnètica, Servei de Radiologia, Vall d'Hebron Hospital Universitari, Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
dc.subject.none.fl_str_mv Cervell - Imatgeria per ressonància magnètica
Imatgeria (Tècnica)
ANATOMY::Nervous System::Central Nervous System::Brain
Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging
ANATOMÍA::sistema nervioso::sistema nervioso central::encéfalo
Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética
topic Cervell - Imatgeria per ressonància magnètica
Imatgeria (Tècnica)
ANATOMY::Nervous System::Central Nervous System::Brain
Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging
ANATOMÍA::sistema nervioso::sistema nervioso central::encéfalo
Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética
description Cervell; Imatge per ressonància magnètica; Aprenentatge transductiu
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
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://hdl.handle.net/11351/6704
http://hdl.handle.net/11351/6704
url https://hdl.handle.net/11351/6704
http://hdl.handle.net/11351/6704
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in Neuroscience;15
https://doi.org/10.3389/fnins.2021.608808
info:eu-repo/grantAgreement/ES/PE2013-2016/DPI2017-86696-R
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv Scientia
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
_version_ 1869418230485876736
spelling Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image SegmentationKushibar, KaisarSalem, MostafaRovira Cañellas, AlexSalvi, JoaquimOliver, ArnauValverde, SergiLlado, XavierCervell - Imatgeria per ressonància magnèticaImatgeria (Tècnica)ANATOMY::Nervous System::Central Nervous System::BrainOther subheadings::Other subheadings::Other subheadings::/diagnostic imagingANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance ImagingANATOMÍA::sistema nervioso::sistema nervioso central::encéfaloOtros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagenTÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnéticaCervell; Imatge per ressonància magnètica; Aprenentatge transductiuCerebro; Imagen de resonancia magnética; Aprendizaje transductivoBrain; Magnetic resonance imaging; Transductive learningSegmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.KK holds FI-DGR2017 grant from the Catalan Government with reference number 2017FI_B00372. This work has been supported by DPI2017-86696-R from the Ministerio de Ciencia y Tecnologia.Frontiers MediaInstitut Català de la Salut[Kushibar K, Valverde S, Salvi J, Oliver A, Lladó X] Institute of Computer Vision and Robotics, University of Girona, Girona, Spain. [Salem M] Institute of Computer Vision and Robotics, University of Girona, Girona, Spain. Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut, Egypt. [Rovira À] Unitat de Ressonància Magnètica, Servei de Radiologia, Vall d'Hebron Hospital Universitari, Barcelona, SpainVall d'Hebron Barcelona Hospital Campus202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/11351/6704http://hdl.handle.net/11351/6704Scientiareponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésFrontiers in Neuroscience;15https://doi.org/10.3389/fnins.2021.608808info:eu-repo/grantAgreement/ES/PE2013-2016/DPI2017-86696-RAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:11351/67042026-05-29T05:05:01Z
score 15,812429

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