Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI

ilustraciones, gráficas, tablas

Autores:: Múnera Garzón, Nicolás

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
dc.title.translated.spa.fl_str_mv	Radiómica utilizando representaciones frecuenciales, casos de estudio: caracterización de trastorno del espectro autista y cáncer de próstata utilizando MRI
title	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
spellingShingle	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI 620 - Ingeniería y operaciones afines Procesamiento de Imagen Asistido por Computador Trastorno Autístico Transtorno Autístico Inteligencia Artificial Image Processing, Computer-Assisted Artificial Intelligence Radiómica Cáncer de próstata Dominio de la frecuencia Trastorno del espectro autista Resonancia magnética Transformada Curvelet Momentos de Zernike Transformada de Fourier Redes neuronales convolucionales Radiomics Prostate cancer Autism spectrum disorder Convolutional neural networks Frequency domain Curvelet transform Fourier transform Deep learning Zernike moments
title_short	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
title_full	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
title_fullStr	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
title_full_unstemmed	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
title_sort	Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI
dc.creator.fl_str_mv	Múnera Garzón, Nicolás
dc.contributor.advisor.none.fl_str_mv	Romero Castro, Edgar Eduardo
dc.contributor.author.none.fl_str_mv	Múnera Garzón, Nicolás
dc.contributor.researchgroup.spa.fl_str_mv	CIM@LAB
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines
topic	620 - Ingeniería y operaciones afines Procesamiento de Imagen Asistido por Computador Trastorno Autístico Transtorno Autístico Inteligencia Artificial Image Processing, Computer-Assisted Artificial Intelligence Radiómica Cáncer de próstata Dominio de la frecuencia Trastorno del espectro autista Resonancia magnética Transformada Curvelet Momentos de Zernike Transformada de Fourier Redes neuronales convolucionales Radiomics Prostate cancer Autism spectrum disorder Convolutional neural networks Frequency domain Curvelet transform Fourier transform Deep learning Zernike moments
dc.subject.decs.spa.fl_str_mv	Procesamiento de Imagen Asistido por Computador Trastorno Autístico Transtorno Autístico Inteligencia Artificial
dc.subject.decs.eng.fl_str_mv	Image Processing, Computer-Assisted Artificial Intelligence
dc.subject.proposal.spa.fl_str_mv	Radiómica Cáncer de próstata Dominio de la frecuencia Trastorno del espectro autista Resonancia magnética Transformada Curvelet Momentos de Zernike Transformada de Fourier Redes neuronales convolucionales
dc.subject.proposal.eng.fl_str_mv	Radiomics Prostate cancer Autism spectrum disorder Convolutional neural networks Frequency domain Curvelet transform Fourier transform Deep learning Zernike moments
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-12T21:57:18Z
dc.date.available.none.fl_str_mv	2021-10-12T21:57:18Z
dc.date.issued.none.fl_str_mv	2021
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/80526
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/80526 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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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Edgar Eduardod49b2499bdf2c07e42f8b4dc9715ef18Múnera Garzón, Nicolása1ca4833b5b0929537159ffc79e9c538CIM@LAB2021-10-12T21:57:18Z2021-10-12T21:57:18Z2021https://repositorio.unal.edu.co/handle/unal/80526Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasRadiomics is a research field in which features from radiological images are extracted to provide non-invasive but reliable quantification with potential usage in personalized medicine. One of its applications is the extraction of sub-visual patterns, which are not commonly analyzed but have high correlation with pathology and require a specific representation to appear. As so, the state of the art has demonstrated the use of higher order transforms a.k.a the frequency domain to obtain such patterns. For this thesis work two use cases that benefited from the use of the frequency domain for radiomic analysis are presented, these are, characterization of prostate cancer and autism spectrum disorder (ASD) from prostate and brain MRI respectively. In both cases gold standard diagnosis protocols do not involve the use of MRI but could benefit from it, as in the case of Prostate Cancer an effective characterization could help to triage prior to a biopsy procedure with tasks like tumor segmentation, classification of normal vs cancerous tissue or automatic tumor staging. Additionally, for ASD an effective characterization from MRI could help to contribute in the study of different manifestations of the spectrum. As a result three contributions in this matter were done: The first one is an adaptive frequency saliency model (AFSM) that sparsely learns a bank of filters in the frequency domain and was used as a preprocessing strategy prior to a transfer learning scheme which classifies cancerous vs healthy tissue in prostate MRI, this method obtains an accuracy of 0.792 ± 0.016 which yields better performance than a baseline experiment without preprocessing that scores 0.776 ± 0.036. The second one is a preliminary study that uses Fourier transform's phase space to study the spatial support of prostate cancerous versus non cancerous tissue. This strategy consisted in a random selection of one subject per class, then, the dataset is preprocessed by replacing the phase of each image by the one of the random selected subject obtaining different preprocessed datasets, and, finally transfer learning models are obtained. Results from this study suggested how spatial support is important for model training. Additionally, a classification improvement was observed when a healthy subject was used for preprocessing obtaining sensitivity and specificity of 0.77 and 0.80 respectively, against a baseline that obtains 0.69 and 0.80 for both metrics. As a third contribution of this thesis, two characterization strategies to differentiate between ASD and control subjects are proposed, these are: Zernike moments and Curvelet Transform under a region-wise analysis. Anatomical brain regions were repersented by a 2D multi slice mapping to analyze first and second order relationships. Both characterization strategies were evaluated under a 10 fold cross validation scheme with children cohorts from the heterogeneous datasets ABIDE I and II. Top performance regions for area under the reciever-operating curve (AUC) were: Left supramarginal gyrus (0.77), Right occipital fusiform cortex (0.76), Right supramarginal gyrus - anterior division (0.75) and Left superior temporal gyrus anterior division (0.77). Additionally the Curvelet approach presented generalizability as a hold out experiment was able to yield an AUC of 0.69 for the Right parahippocampal gyrus - posterior division. This representation also showed no correlation with other state of the art techniques representing a contribution to ASD characterization with structural MRI.Radiómica es un área de investigación en la que se extraen patrones de imágenes radiológicas con el objetivo de lograr una cuantificación no invasiva y confiable con usos potenciales en medicina personalizada. Una aplicación común es la extracción de patrones sub-visuales; éstos, no son fáciles de apreciar o analizar en campo y requieren de un cambio de dominio para poderlos apreciar. El estado del arte en el área ha demostrado cómo el uso de transformaciones de alto orden también nombradas en la literatura como representaciones frecuenciales son aptas para obtener patrones sub-visuales, de manera que éste trabajo de tesis se enfocó en la aplicación de radiómica utilizando el dominio de la frecuencia para dos casos de estudio: caracterización de cancer de próstata y trastorno del espectro autista (TEA) a partir de imágenes de resonancia magnética estructural de próstata y cerebro. El protocolo estándar de diagnóstico para ambos casos no incluye la toma de resonancia magnética, sin embargo una caracterización adecuada de esta fuente de información no invasiva puede traer ventajas sustanciales como por ejemplo en el caso de cancer de próstata, servir de triage antes de un procedimiento de biopsia transrectal realizando tareas como segmentación de tumores, clasificación de tejido sano vs cáncer o detección de la agresividad del cáncer en tejido. Mientras que en el caso del TEA esta caracterización contribuye al estudio de diferentes manifestaciones del espectro autista. Como resultado tres contribuciones se realizaron: La primera es un método adaptativo de saliencia en el dominio de la frecuencia (AFSM) que de manera sparse aprende un banco de filtros en el dominio de la frecuencia y se utilizó como estrategia de preprocesamiento previo a la clasificación via transfer learning entre tejido sano y cancer. Este método obtiene un accuracy de 0.792 ± 0.016 superando una línea de base que obtiene 0.776 ± 0.036 . La segunda contribución es un estudio preliminar que utiliza el espacio de fase de la transformada de Fourier para para estudiar el soporte espacial de tejido canceroso y no canceroso en resonancia de próstata. Esta estrategia consiste en la selección de un sujeto aleatorio por clase, luego, la base de datos se preprocesa reemplazando la fase de todos los sujetos por la de cada sujeto escogido de manera aleatoria, obteniendo versiones modificadas de la base de datos que son sometidas a un esquema de clasificación utilizando transfer learning. Los resultados de este trabajo sugieren cómo el soporte espacial es importante en el entranamiento de cualquier modelo. Adicionalmente, se observó una mejora en clasificación cuando se utilizó tejido sano en el preprocesamiento, es decir, mejora de una línea de base con sensibilidad y especificidad de 0.69 y 0.80 respectivamente, a 0.77 0.80 en las imágenes preprocesadas. Como última contribución, se propusieron dos estrategias de caracterización para diferencias entre sujetos con TEA y control en imagen de resonancia, estas fueron: momentos de Zernike y transformada Curvelet. Ambas estrategias se realizaron bajo una representación 2D de cada región cerebral y fueron evaluadas utilizando validación cruzada 10 fold en dos cohortes infantiles de las bases de datos ABIDE I y II. Las regiones con mejor rendimiento en la métrica Área bajo la curva ROC (AUC) fueron: giro supramarginal izquierdo (0.77), corteza fusiforme occipital derecha (0.76), giro supramarginal derecho - división anterior (0.75) y giro temporal superior izquierdo - divisón anterior (0.77). Adicional a esto, el enfoque con la transformada Curvelet presentó generalización obteniendo un AUC de 0.69 para un experimento hold out en la región: giro parahipocampal derecho - división posterior. Además, esta repesentación no mostró ningún tipo de correlación con otras técnicas del estado del arte, representando una contribución al estado del arte en el área. (Texto tomado de la fuente).Incluye anexosMaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónRadiómicaxvii, 88 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afinesProcesamiento de Imagen Asistido por ComputadorTrastorno AutísticoTranstorno AutísticoInteligencia ArtificialImage Processing, Computer-AssistedArtificial IntelligenceRadiómicaCáncer de próstataDominio de la frecuenciaTrastorno del espectro autistaResonancia magnéticaTransformada CurveletMomentos de ZernikeTransformada de FourierRedes neuronales convolucionalesRadiomicsProstate cancerAutism spectrum disorderConvolutional neural networksFrequency domainCurvelet transformFourier transformDeep learningZernike momentsRadiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRIRadiómica utilizando representaciones frecuenciales, casos de estudio: caracterización de trastorno del espectro autista y cáncer de próstata utilizando MRITrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM9287, S. 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Radiomics using frequency representations, cases of study: autism spectrum disorder and cancer characterization through MRI

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