Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models

ilustraciones, diagramas, tablas

Autores:: Laiton Bonadiez, Camilo Andres

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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repository_id_str
dc.title.eng.fl_str_mv	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
dc.title.translated.spa.fl_str_mv	Método para la segmentación de imágenes de resonancia magnética cerebrales usando una arquitectura de red neuronal basada en modelos de atención
title	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
spellingShingle	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 610 - Medicina y salud::611 - Anatomía humana, citología, histología Magnetic resonance imaging Diagnostic imaging Resonancia magnética en imágenes Diagnóstico por imágenes Medical image segmentation Deep learning Transformers Convolutional neural networks Brain structures Segmentación de imágenes médicas Aprendizaje profundo Redes neuronales convolucionales Transformers Estructuras cerebrales
title_short	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
title_full	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
title_fullStr	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
title_full_unstemmed	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
title_sort	Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models
dc.creator.fl_str_mv	Laiton Bonadiez, Camilo Andres
dc.contributor.advisor.none.fl_str_mv	Sanchez Torres, German Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv	Laiton Bonadiez, Camilo Andres
dc.contributor.researchgroup.spa.fl_str_mv	Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 610 - Medicina y salud::611 - Anatomía humana, citología, histología
topic	000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 610 - Medicina y salud::611 - Anatomía humana, citología, histología Magnetic resonance imaging Diagnostic imaging Resonancia magnética en imágenes Diagnóstico por imágenes Medical image segmentation Deep learning Transformers Convolutional neural networks Brain structures Segmentación de imágenes médicas Aprendizaje profundo Redes neuronales convolucionales Transformers Estructuras cerebrales
dc.subject.lemb.eng.fl_str_mv	Magnetic resonance imaging Diagnostic imaging
dc.subject.lemb.spa.fl_str_mv	Resonancia magnética en imágenes Diagnóstico por imágenes
dc.subject.proposal.eng.fl_str_mv	Medical image segmentation Deep learning Transformers Convolutional neural networks Brain structures
dc.subject.proposal.spa.fl_str_mv	Segmentación de imágenes médicas Aprendizaje profundo Redes neuronales convolucionales Transformers Estructuras cerebrales
description	ilustraciones, diagramas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-10-19T15:23:04Z
dc.date.available.none.fl_str_mv	2022-10-19T15:23:04Z
dc.date.issued.none.fl_str_mv	2022-07-31
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82378
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82378 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
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rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemas
dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sanchez Torres, German4b698f4e03454cea5a4cc7c24825b47a600Branch Bedoya, John Willian8373bc4285cc9e2e59e8f540f737e1db600Laiton Bonadiez, Camilo Andres7cc1768e9ff944533283233db2664234600Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial2022-10-19T15:23:04Z2022-10-19T15:23:04Z2022-07-31https://repositorio.unal.edu.co/handle/unal/82378Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasIn recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling operations, causing a loss in valuable data and focusing on local information. In this thesis, we propose a deep learning-based approach that uses global and local features which are of importance in the medical image segmentation process. In order to train the architecture, we used extracted three-dimensional (3D) blocks from the full magnetic resonance image resolution, which were sent through a set of successive convolutional neural network (CNN) layers free of pooling operations to extract local information. Later, we sent the resulting feature maps to successive layers of self-attention modules to obtain the global context, whose output was later dispatched to the decoder pipeline composed mostly of upsampling layers. The model was trained using the Mindboggle-101 dataset. The experimental results showed that the self-attention modules allow segmentation with a higher Mean Dice Score of 0.90 ± 0.036 compared with other UNet-based approaches. The average segmentation time was approximately 0.032 s per brain structure. The proposed model allows tackling the brain structure segmentation task properly. Exploiting the global context that the self-attention modules incorporate allows for more precise and faster segmentation. We segmented 37 brain structures and, to the best of our knowledge, it is the largest number of structures under a 3D approach using attention mechanisms.En los últimos años, el uso de modelos basados en aprendizaje profundo para el desarrollo de sistemas de salud avanzados ha ido en aumento debido a los excelentes resultados que pueden alcanzar. Sin embargo, la mayoría de los modelos de aprendizaje profundo propuestos utilizan, en gran medida, operaciones convolucionales y de pooling, lo que provoca una pérdida de datos valiosos centrándose principalmente en la información local. En esta tesis, proponemos un enfoque basado en el aprendizaje profundo que utiliza características globales y locales que son importantes en el proceso de segmentación de imágenes médicas. Para entrenar la arquitectura, utilizamos bloques tridimensionales (3D) extraídos de la resolución completa de la imagen de resonancia magnética. Estas se enviaron a través de un conjunto de capas sucesivas de redes neuronales convolucionales (CNN) libres de operaciones de pooling para extraer información local. Luego, enviamos los mapas de características resultantes a capas sucesivas de módulos de autoatención para obtener el contexto global, cuya salida se envió más tarde a la canalización del decodificador compuesta principalmente por capas de upsampling. El modelo fue entrenado usando el conjunto de datos Mindboggle-101. Los resultados experimentales mostraron que los módulos de autoatención permiten la segmentación con un Mean Dice Score 0,90 ± 0,036 la cual es mayor en comparación con otros enfoques basados en UNet. El tiempo medio de segmentación fue de aproximadamente 0,032 s por estructura cerebral. El modelo propuesto permite abordar adecuadamente la tarea de segmentación de estructuras cerebrales. Así mismo, permite aprovechar el contexto global que incorporan los módulos de autoatención logrando una segmentación más precisa y rápida. En este trabajo segmentamos 37 estructuras cerebrales y, según nuestro conocimiento, es el mayor número de estructuras bajo un enfoque 3D utilizando mecanismos de atención. (Texto tomado de la fuente)MaestríaMagíster en Ingeniería - Ingeniería de SistemasLa metodología constó de 4 fases. La primera se concentró en Determinar el conjunto de imágenes de resonancia magnética que fueron parte del conjunto de datos y seleccionar el conjunto de estructuras anatómicas a segmentar. La segunda fase estuvo dirigida a Definir el conjunto de técnicas a emplear en el preprocesamiento de las imágenes de resonancia magnética definidas. La tercera fase se concentró en Diseñar la arquitectura de red neuronal para la segmentación de las estructuras anatómicas cerebrales y por último, la cuarta fase estuvo dirigida a evaluar y comparar el modelo de red neuronal implementado con aquellos existentes en el estado del arte.Visión artificial y aprendizaje automáticoÁrea Curricular de Ingeniería de Sistemas e Informáticaxi, 54 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - Sistemas000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación610 - Medicina y salud::611 - Anatomía humana, citología, histologíaMagnetic resonance imagingDiagnostic imagingResonancia magnética en imágenesDiagnóstico por imágenesMedical image segmentationDeep learningTransformersConvolutional neural networksBrain structuresSegmentación de imágenes médicasAprendizaje profundoRedes neuronales convolucionalesTransformersEstructuras cerebralesMethod for the segmentation of brain magnetic resonance images using a neural network architecture based on attention modelsMétodo para la segmentación de imágenes de resonancia magnética cerebrales usando una arquitectura de red neuronal basada en modelos de atenciónTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMM. 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Wachinger, “Quicknat: A fully convolutional network for quick and accurate segmentation of neuroanatomy,” NeuroImage, vol. 186, pp. 713–727, 2019.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82378/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1016093055.2022.pdf1016093055.2022.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf14112182https://repositorio.unal.edu.co/bitstream/unal/82378/2/1016093055.2022.pdfe5acc38401dd7d55eee8c85f9915efe8MD52THUMBNAIL1016093055.2022.pdf.jpg1016093055.2022.pdf.jpgGenerated Thumbnailimage/jpeg4549https://repositorio.unal.edu.co/bitstream/unal/82378/3/1016093055.2022.pdf.jpg11ef968d33573033a449d8bd184aedb7MD53unal/82378oai:repositorio.unal.edu.co:unal/823782023-10-06 18:05:28.237Repositorio Institucional Universidad Nacional de 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Method for the segmentation of brain magnetic resonance images using a neural network architecture based on attention models

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