Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/BIBM49941.2020.9313244 http://hdl.handle.net/11449/208367 |
Resumo: | Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm3. We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS. |
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Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networksbrain lesions quantificationCNNMRImultiple sclerosisMagnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm3. We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS.State University (UNESP) School of Sciences-São PauloMedical School- São Paulo State University (UNESP)Isep School of Engineering - Polytechnic of PortoInesc Tec and Faculty of Engineering University of PortoState University (UNESP) School of Sciences-São PauloMedical School- São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)School of Engineering - Polytechnic of PortoUniversity of PortoOliveira, Marcela De [UNESP]Santinelli, Felipe Balistieri [UNESP]Piacenti-Silva, Marina [UNESP]Rocha, Fernando Coronetti Gomes [UNESP]Barbieri, Fabio Augusto [UNESP]Lisboa-Filho, Paulo Noronha [UNESP]Santos, Jorge ManuelCardoso, Jaime Dos Santos2021-06-25T11:11:02Z2021-06-25T11:11:02Z2020-12-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2045-2048http://dx.doi.org/10.1109/BIBM49941.2020.9313244Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2045-2048.http://hdl.handle.net/11449/20836710.1109/BIBM49941.2020.93132442-s2.0-85100338002Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020info:eu-repo/semantics/openAccess2024-04-24T18:53:53Zoai:repositorio.unesp.br:11449/208367Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:25:02.079072Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
title |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
spellingShingle |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks Oliveira, Marcela De [UNESP] brain lesions quantification CNN MRI multiple sclerosis |
title_short |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
title_full |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
title_fullStr |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
title_full_unstemmed |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
title_sort |
Quantification of Brain Lesions in Multiple Sclerosis Patients using Segmentation by Convolutional Neural Networks |
author |
Oliveira, Marcela De [UNESP] |
author_facet |
Oliveira, Marcela De [UNESP] Santinelli, Felipe Balistieri [UNESP] Piacenti-Silva, Marina [UNESP] Rocha, Fernando Coronetti Gomes [UNESP] Barbieri, Fabio Augusto [UNESP] Lisboa-Filho, Paulo Noronha [UNESP] Santos, Jorge Manuel Cardoso, Jaime Dos Santos |
author_role |
author |
author2 |
Santinelli, Felipe Balistieri [UNESP] Piacenti-Silva, Marina [UNESP] Rocha, Fernando Coronetti Gomes [UNESP] Barbieri, Fabio Augusto [UNESP] Lisboa-Filho, Paulo Noronha [UNESP] Santos, Jorge Manuel Cardoso, Jaime Dos Santos |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) School of Engineering - Polytechnic of Porto University of Porto |
dc.contributor.author.fl_str_mv |
Oliveira, Marcela De [UNESP] Santinelli, Felipe Balistieri [UNESP] Piacenti-Silva, Marina [UNESP] Rocha, Fernando Coronetti Gomes [UNESP] Barbieri, Fabio Augusto [UNESP] Lisboa-Filho, Paulo Noronha [UNESP] Santos, Jorge Manuel Cardoso, Jaime Dos Santos |
dc.subject.por.fl_str_mv |
brain lesions quantification CNN MRI multiple sclerosis |
topic |
brain lesions quantification CNN MRI multiple sclerosis |
description |
Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm3. We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-16 2021-06-25T11:11:02Z 2021-06-25T11:11:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/BIBM49941.2020.9313244 Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2045-2048. http://hdl.handle.net/11449/208367 10.1109/BIBM49941.2020.9313244 2-s2.0-85100338002 |
url |
http://dx.doi.org/10.1109/BIBM49941.2020.9313244 http://hdl.handle.net/11449/208367 |
identifier_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2045-2048. 10.1109/BIBM49941.2020.9313244 2-s2.0-85100338002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2045-2048 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808128646415646720 |