Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques

This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation...

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Detalles Bibliográficos
Autor: Kushibar, Kaisar
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:CBUC, CESCA
Repositorio:TDR. Tesis Doctorales en Red
OAI Identifier:oai:www.tdx.cat:10803/670766
Acceso en línea:http://hdl.handle.net/10803/670766
Access Level:acceso abierto
Palabra clave:Brain structures
Estructures cerebrals
Estructuras cerebrales
MRI
Magnetic resonance imaging
Imatgeria per ressonància magnètica
Imágenes por resonancia magnética
Deep learning
Aprenentatge profund
Aprendizaje profundo
Subcortical structures
Estructures subcorticals
Estructuras subcorticales
CNN
Convolutional neural network
Xarxa neuronal convolucional
Red neuronal convolucional
004
616.8
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oai_identifier_str oai:www.tdx.cat:10803/670766
network_acronym_str ES
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repository_id_str
spelling Automatic segmentation of brain structures in magnetic resonance images using deep learning techniquesKushibar, KaisarBrain structuresEstructures cerebralsEstructuras cerebralesMRIMagnetic resonance imagingImatgeria per ressonància magnèticaImágenes por resonancia magnéticaDeep learningAprenentatge profundAprendizaje profundoSubcortical structuresEstructures subcorticalsEstructuras subcorticalesCNNConvolutional neural networkXarxa neuronal convolucionalRed neuronal convolucional004616.8This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation technique to improve the robustness and consistency of deep learning model. Third, an unsupervised domain adaptation method was proposed to eliminate the requirement of manual intervention to train a deep learning model that is robust to differences in the MRI images from multi-centre and multi-scanner datasets. The experimental results for all the proposals demonstrated the effectiveness of our approaches in accurately segmenting the sub-cortical brain structures and has shown state-of-the-art performance on well-known publicly available datasetsEsta tesis doctoral se centra en el desarrollo de métodos basados en el aprendizaje profundo para la segmentación precisa de las estructuras cerebrales subcorticales a partir de la resonancia magnética. En primer lugar, hemos propuesto una arquitectura 2.5D CNN que combina características convolucionales y espaciales. En segundo lugar, hemos propuesto una técnica de adaptación de dominio supervisada para mejorar la robustez y la consistencia del modelo de aprendizaje profundo. En tercer lugar, hemos propuesto un método de adaptación de dominio no supervisado para eliminar el requisito de intervención manual para entrenar un modelo de aprendizaje profundo que sea robusto a las diferencias en las imágenes de la resonancia magnética de los conjuntos de datos multicéntricos y multiescáner. Los resultados experimentales de todas las propuestas demostraron la eficacia de nuestros enfoques para segmentar con precisión las estructuras cerebrales subcorticales y han mostrado un rendimiento de vanguardia en los conocidos conjuntos de datos de acceso públicoUniversitat de GironaValverde Valverde, SergiOliver i Malagelada, ArnauLladó Bardera, XavierUniversitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors202120212020info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion80 p.application/pdfapplication/pdfhttp://hdl.handle.net/10803/670766TDX (Tesis Doctorals en Xarxa)reponame:TDR. Tesis Doctorales en Redinstname:CBUC, CESCAInglésL'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:www.tdx.cat:10803/6707662026-06-14T12:46:07Z
dc.title.none.fl_str_mv Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
title Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
spellingShingle Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
Kushibar, Kaisar
Brain structures
Estructures cerebrals
Estructuras cerebrales
MRI
Magnetic resonance imaging
Imatgeria per ressonància magnètica
Imágenes por resonancia magnética
Deep learning
Aprenentatge profund
Aprendizaje profundo
Subcortical structures
Estructures subcorticals
Estructuras subcorticales
CNN
Convolutional neural network
Xarxa neuronal convolucional
Red neuronal convolucional
004
616.8
title_short Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
title_full Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
title_fullStr Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
title_full_unstemmed Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
title_sort Automatic segmentation of brain structures in magnetic resonance images using deep learning techniques
dc.creator.none.fl_str_mv Kushibar, Kaisar
author Kushibar, Kaisar
author_facet Kushibar, Kaisar
author_role author
dc.contributor.none.fl_str_mv Valverde Valverde, Sergi
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
Universitat de Girona. Departament d'Arquitectura i Tecnologia de Computadors
dc.subject.none.fl_str_mv Brain structures
Estructures cerebrals
Estructuras cerebrales
MRI
Magnetic resonance imaging
Imatgeria per ressonància magnètica
Imágenes por resonancia magnética
Deep learning
Aprenentatge profund
Aprendizaje profundo
Subcortical structures
Estructures subcorticals
Estructuras subcorticales
CNN
Convolutional neural network
Xarxa neuronal convolucional
Red neuronal convolucional
004
616.8
topic Brain structures
Estructures cerebrals
Estructuras cerebrales
MRI
Magnetic resonance imaging
Imatgeria per ressonància magnètica
Imágenes por resonancia magnética
Deep learning
Aprenentatge profund
Aprendizaje profundo
Subcortical structures
Estructures subcorticals
Estructuras subcorticales
CNN
Convolutional neural network
Xarxa neuronal convolucional
Red neuronal convolucional
004
616.8
description This PhD thesis focuses on the development of deep learning based methods for accurate segmentation of the sub-cortical brain structures from MRI. First, we have proposed a 2.5D CNN architecture that combines convolutional and 2/2 spatial features. Second, we proposed a supervised domain adaptation technique to improve the robustness and consistency of deep learning model. Third, an unsupervised domain adaptation method was proposed to eliminate the requirement of manual intervention to train a deep learning model that is robust to differences in the MRI images from multi-centre and multi-scanner datasets. The experimental results for all the proposals demonstrated the effectiveness of our approaches in accurately segmenting the sub-cortical brain structures and has shown state-of-the-art performance on well-known publicly available datasets
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10803/670766
url http://hdl.handle.net/10803/670766
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 80 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat de Girona
publisher.none.fl_str_mv Universitat de Girona
dc.source.none.fl_str_mv TDX (Tesis Doctorals en Xarxa)
reponame:TDR. Tesis Doctorales en Red
instname:CBUC, CESCA
instname_str CBUC, CESCA
reponame_str TDR. Tesis Doctorales en Red
collection TDR. Tesis Doctorales en Red
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421003244830720
score 15,300719

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