Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences

ilustraciones, fotografías, graficas

Autores:: Jaramillo González, María

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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oai_identifier_str	oai:repositorio.unal.edu.co:unal/83139
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
dc.title.translated.spa.fl_str_mv	Detección de tumores pancreáticos malignos basado en la caracterización de textura durante secuencias de video de ultrasonido endoscópico
title	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
spellingShingle	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences 610 - Medicina y salud 620 - Ingeniería y operaciones afines Neoplasias Pancreáticas Diagnóstico por Imagen Pancreatic Neoplasms Diagnostic Imaging Pancreatic cancer Adenocarcinoma Detection Differentiation Endoscopic ultrasound Echoendoscopy Image classification Cáncer de páncreas Adenocarcinoma Detección Diferenciación Ecoendoscopia Clasificación de imágenes
title_short	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
title_full	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
title_fullStr	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
title_full_unstemmed	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
title_sort	Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences
dc.creator.fl_str_mv	Jaramillo González, María
dc.contributor.advisor.none.fl_str_mv	Romero Castro, Edgar Eduardo
dc.contributor.author.none.fl_str_mv	Jaramillo González, María
dc.contributor.researcher.none.fl_str_mv	Gómez Zuleta, Martín Alonso
dc.contributor.researchgroup.spa.fl_str_mv	Cim@Lab
dc.contributor.cvlac.spa.fl_str_mv	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001598592
dc.contributor.researchgate.spa.fl_str_mv	https://www.researchgate.net/profile/Maria-Gonzalez-468
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud 620 - Ingeniería y operaciones afines
topic	610 - Medicina y salud 620 - Ingeniería y operaciones afines Neoplasias Pancreáticas Diagnóstico por Imagen Pancreatic Neoplasms Diagnostic Imaging Pancreatic cancer Adenocarcinoma Detection Differentiation Endoscopic ultrasound Echoendoscopy Image classification Cáncer de páncreas Adenocarcinoma Detección Diferenciación Ecoendoscopia Clasificación de imágenes
dc.subject.other.spa.fl_str_mv	Neoplasias Pancreáticas Diagnóstico por Imagen
dc.subject.other.eng.fl_str_mv	Pancreatic Neoplasms Diagnostic Imaging
dc.subject.proposal.eng.fl_str_mv	Pancreatic cancer Adenocarcinoma Detection Differentiation Endoscopic ultrasound Echoendoscopy Image classification
dc.subject.proposal.spa.fl_str_mv	Cáncer de páncreas Adenocarcinoma Detección Diferenciación Ecoendoscopia Clasificación de imágenes
description	ilustraciones, fotografías, graficas
publishDate	2022
dc.date.issued.none.fl_str_mv	2022
dc.date.accessioned.none.fl_str_mv	2023-01-26T14:06:30Z
dc.date.available.none.fl_str_mv	2023-01-26T14:06:30Z
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/83139
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/83139 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-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xvi, 77 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Medicina - Maestría en Ingeniería Biomédica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Medicina
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Edgar Eduardod49b2499bdf2c07e42f8b4dc9715ef18Jaramillo González, María77c84f0374987295175e6a938622011aGómez Zuleta, Martín AlonsoCim@Labhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001598592https://www.researchgate.net/profile/Maria-Gonzalez-4682023-01-26T14:06:30Z2023-01-26T14:06:30Z2022https://repositorio.unal.edu.co/handle/unal/83139Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, graficasPancreatic Cancer (PC) is one of the most aggressive cancers, constituting the seventh leading cause of cancer-related death globally in 2020. Usually, the asymptomatic response of PC causes the delayed diagnosis of the disease. Diagnosis of PC usually includes ultrasonography (US), computed tomography (CT), magnetic resonance (MRI), and endoscopic ultrasound (EUS). Although EUS is the diagnostic method with the highest sensitivity reported, the procedure is highly operator-dependent. A gastroenterologist requires more than 150 supervised procedures to interpret the anatomy blurred by several noise sources. Therefore, a second reader may be desirable to support the procedure and assist the training process in a gastroenterology service. Some computational strategies have been developed to detect PC in EUS images, but those methods are semi-automatic in practice and very susceptible to noise. Hence, the main contribution of this work is an automatic strategy to detect PC in complete video sequences of EUS procedures. The proposed methodology describes the mixture of echo patterns using the Speeded-Up Robust Features (SURF) method. A set of interest points are defined and described correlating the echo patterns in a multiscale analysis, and filtering the noise sources, usually uncorrelated among different scales. Then, images with PC are differentiated by a binary classification method, evaluating Support Vector Machines and Adaboost models. Additionally, the proposed method is assessed using a public EUS database constructed and released in this work, with 55 cases. Finally, the proposed method was compared with typical Deep Learning approaches, reaching an accuracy of 92.1\% and 90.0\%, respectively. In addition, the method herein proposed is also stable in experiments with added noise, while the nets fail to maintain a similar performance.El Cáncer de Páncreas (CP) fue la séptima causa de muerte por cáncer en el mundo en 2020. Es uno de los más agresivos y en la mayoría de los casos se diagnostica en etapas avanzadas por su respuesta asintomática. El diagnóstico del CP se realiza mediante técnicas de imágen como ultrasonido (US), tomografía computarizada(TAC), resonancia magnética(RMN) y Ecoendoscopia(EE). Aunque la EE tiene la más alta sensibilidad, el proceso de entrenamiento de los especialistas requiere más de 150 procedimientos supervisados, convirtiendose en un procedimiento altamente dependiente de la experticia del gastroenterólogo y del manejo de las múltiples fuentes de ruido durante el procedimiento. Por lo tanto, es deseable un segundo lector para apoyar el procedimiento y asistir el proceso de entrenamiento. Se han desarrollado estrategias computacionales para apoyar la detección del CP, pero son semi-automáticos en la práctica y altamente suceptibles a las fuentes de ruido. La principal contribución de este trabajo es el desarrollo de una estrategia automática para detectar CP en secuencias de video completas de procedimientos de EE. El método describe los eco-patrones en imágenes de EE utilizando el algoritmo “SURF” por sus siglas en inglés. Se definen y describen un conjunto de puntos de interés correlacionados en un análisis multiecala y se filtran las fuentes de ruido que usualmente no se correlacionan entre escalas. Luego, las imágenes con CP se diferencian mediante una clasificación binaria utilizando métodos de soporte vectorial y árboles de decisión. Adicionalmente, el método se evalúa utilizando una base de datos pública construida en este trabajo con 55 casos en total. Finalmente, el rendimiento se compara con los enfoques típicos de aprendizaje profundo, obteniendo un rendimiento de 92.1\% y 90.0\%, respectivamente. Adicionalmente, el metodo propuesto es estable en experimentos al adicionar ruido, en los que las redes fallan en mantener un rendimiento similar. (Texto tomado de la fuente)Further author information: (Send correspondence to María Jaramillo) Diego Bravo: E-mail: marjaramillogon@unal.edu.co, Telephone: +57 3137214469MaestríaMagíster en Ingeniería BiomédicaDigital Anatomy by Images ResearchApplied Computing - Image Processingxvi, 77 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud620 - Ingeniería y operaciones afinesNeoplasias PancreáticasDiagnóstico por ImagenPancreatic NeoplasmsDiagnostic ImagingPancreatic cancerAdenocarcinomaDetectionDifferentiationEndoscopic ultrasoundEchoendoscopyImage classificationCáncer de páncreasAdenocarcinomaDetecciónDiferenciaciónEcoendoscopiaClasificación de imágenesDetection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequencesDetección de tumores pancreáticos malignos basado en la caracterización de textura durante secuencias de video de ultrasonido endoscópicoTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[1] Akiba, Takuya ; Sano, Shotaro ; Yanase, Toshihiko ; Ohta, Takeru ; Koyama, Masanori: Optuna: A Next-generation Hyperparameter Optimization Framework, 2019. 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En: PLOS ONE 8 (2013), 05, Nr. 5, p. 1-6EstudiantesGrupos comunitariosInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83139/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1053835990.2022.pdf1053835990.2022.pdfTesis de Maestría en Ingeniería Biomédicaapplication/pdf16180863https://repositorio.unal.edu.co/bitstream/unal/83139/2/1053835990.2022.pdf988bceab03eb58b773c20f60e7ee2a8bMD52THUMBNAIL1053835990.2022.pdf.jpg1053835990.2022.pdf.jpgGenerated Thumbnailimage/jpeg4795https://repositorio.unal.edu.co/bitstream/unal/83139/3/1053835990.2022.pdf.jpg591512404031a9b9cba0356baa2bb2efMD53unal/83139oai:repositorio.unal.edu.co:unal/831392024-08-15 23:15:08.293Repositorio Institucional Universidad Nacional de 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Detection of pancreatic malignant tumors based on texture characterization during endoscopy ultrasound video sequences

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