Cargando…

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

Segmentation 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kushibar, Kaisar, Salem, Mostafa, Valverde, Sergi, Rovira, Àlex, Salvi, Joaquim, Oliver, Arnau, Lladó, Xavier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116893/
https://www.ncbi.nlm.nih.gov/pubmed/33994917
http://dx.doi.org/10.3389/fnins.2021.608808
_version_ 1783691494969835520
author Kushibar, Kaisar
Salem, Mostafa
Valverde, Sergi
Rovira, Àlex
Salvi, Joaquim
Oliver, Arnau
Lladó, Xavier
author_facet Kushibar, Kaisar
Salem, Mostafa
Valverde, Sergi
Rovira, Àlex
Salvi, Joaquim
Oliver, Arnau
Lladó, Xavier
author_sort Kushibar, Kaisar
collection PubMed
description Segmentation 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.
format Online
Article
Text
id pubmed-8116893
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-81168932021-05-14 Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation Kushibar, Kaisar Salem, Mostafa Valverde, Sergi Rovira, Àlex Salvi, Joaquim Oliver, Arnau Lladó, Xavier Front Neurosci Neuroscience Segmentation 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. Frontiers Media S.A. 2021-04-29 /pmc/articles/PMC8116893/ /pubmed/33994917 http://dx.doi.org/10.3389/fnins.2021.608808 Text en Copyright © 2021 Kushibar, Salem, Valverde, Rovira, Salvi, Oliver and Lladó. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Kushibar, Kaisar
Salem, Mostafa
Valverde, Sergi
Rovira, Àlex
Salvi, Joaquim
Oliver, Arnau
Lladó, Xavier
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
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_short 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
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116893/
https://www.ncbi.nlm.nih.gov/pubmed/33994917
http://dx.doi.org/10.3389/fnins.2021.608808
work_keys_str_mv AT kushibarkaisar transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT salemmostafa transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT valverdesergi transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT roviraalex transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT salvijoaquim transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT oliverarnau transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation
AT lladoxavier transductivetransferlearningfordomainadaptationinbrainmagneticresonanceimagesegmentation