Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans
ilustraciones, gráficas, tablas
- Autores:
-
Sánchez Legarda, Yeison David
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81123
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales
Deep Learning
Retinal Diseases
Tomography, Optical
Aprendizaje profundo
Enfermedades de la Retina
Tomografía Óptica
Computer vision
Deep learning
Machine learning
Optical coherence tomography scans
Biomarkers segmentation
Retinal diseases classification
Aprendizaje profundo
Tomografía de coherencia óptica
Visión por computador
Aprendizaje de máquinas
Segmentación de biomarcadores
Clasificación de enfermedades retinianas
Redes neuronales generativas adversarias
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| dc.title.translated.spa.fl_str_mv |
Enfoque de aprendizaje profundo para identificar enfermedades y biomarcadores en imagenes de tomografía de coherencia óptica |
| title |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| spellingShingle |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans 000 - Ciencias de la computación, información y obras generales Deep Learning Retinal Diseases Tomography, Optical Aprendizaje profundo Enfermedades de la Retina Tomografía Óptica Computer vision Deep learning Machine learning Optical coherence tomography scans Biomarkers segmentation Retinal diseases classification Aprendizaje profundo Tomografía de coherencia óptica Visión por computador Aprendizaje de máquinas Segmentación de biomarcadores Clasificación de enfermedades retinianas Redes neuronales generativas adversarias |
| title_short |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| title_full |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| title_fullStr |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| title_full_unstemmed |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| title_sort |
Deep learning approach to identify diseases and biomarkers in optical coherence tomography scans |
| dc.creator.fl_str_mv |
Sánchez Legarda, Yeison David |
| dc.contributor.advisor.spa.fl_str_mv |
González Osorio, Fabio Augusto Perdomo Charry, Oscar Julian |
| dc.contributor.author.spa.fl_str_mv |
Sánchez Legarda, Yeison David |
| dc.contributor.researchgroup.spa.fl_str_mv |
Mindlab |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales |
| topic |
000 - Ciencias de la computación, información y obras generales Deep Learning Retinal Diseases Tomography, Optical Aprendizaje profundo Enfermedades de la Retina Tomografía Óptica Computer vision Deep learning Machine learning Optical coherence tomography scans Biomarkers segmentation Retinal diseases classification Aprendizaje profundo Tomografía de coherencia óptica Visión por computador Aprendizaje de máquinas Segmentación de biomarcadores Clasificación de enfermedades retinianas Redes neuronales generativas adversarias |
| dc.subject.decs.eng.fl_str_mv |
Deep Learning Retinal Diseases Tomography, Optical |
| dc.subject.decs.spa.fl_str_mv |
Aprendizaje profundo Enfermedades de la Retina Tomografía Óptica |
| dc.subject.proposal.eng.fl_str_mv |
Computer vision Deep learning Machine learning Optical coherence tomography scans Biomarkers segmentation Retinal diseases classification |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje profundo Tomografía de coherencia óptica Visión por computador Aprendizaje de máquinas Segmentación de biomarcadores Clasificación de enfermedades retinianas Redes neuronales generativas adversarias |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-03T16:30:51Z |
| dc.date.available.none.fl_str_mv |
2022-03-03T16:30:51Z |
| 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 |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81123 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81123 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 |
| dc.relation.references.spa.fl_str_mv |
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Marion Funk, Katharina Kriechbaum, Franz Prager, Thomas Benesch, Michael Georgopoulos, Gerhard J Zlabinger, and Ursula Schmidt-Erfurth. Intraocular Concentrations of Growth Factors and Cytokines in Retinal Vein Occlusion and the Effect of Therapy with Bevacizumab. Investigative Ophthalmology Visual Science, 50(3):1025–1032, mar 2009 Paris G Tranos, Sanjeewa S Wickremasinghe, Nikos T Stangos, Fotis Topouzis, Ioannis Tsinopoulos, and Carlos E Pavesio. Macular edema. Survey of Ophthalmology, 49(5):470–490, 2004 Marion R Munk, Stefan Sacu, Wolfgang Huf, Florian Sulzbacher, Tamara J Mitterm¨uller, Katharina Eibenberger, Sandra Rezar, Matthias Bolz, Christopher G Kiss, Christian Simader, and Others. Differential diagnosis of macular edema of different pathophysiologic origins by spectral domain optical coherence tomography. 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Association Between Soluble CD14 in the Aqueous Humor and Hyperreflective Foci on Optical Coherence Tomography in Patients With Diabetic Macular Edema. Investigative Ophthalmology Visual Science, 59(2):715–721, feb 2018 Glenn Yiu, R Joel Welch, Yinwen Wang, Zhe Wang, Pin-Wen Wang, and Zdenka Haskova. Spectral-Domain OCT Predictors of Visual Outcomes after Ranibizumab Treatment for Macular Edema Resulting from Retinal Vein Occlusion. Ophthalmology Retina, 4(1):67–76, 2020 D Jha, P H Smedsrud, M A Riegler, D Johansen, T D Lange, P Halvorsen, and H D. Johansen. ResUNet++: An Advanced Architecture for Medical Image Segmentation. In 2019 IEEE International Symposium on Multimedia (ISM), pages 225–230, 2019 Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018 |
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Universidad Nacional de Colombia |
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Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación |
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Departamento de Ingeniería de Sistemas e Industrial |
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Facultad de Ingeniería |
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Universidad Nacional de Colombia - Sede Bogotá |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto0e9d70b5c1d7448338ca4467ccb27e59Perdomo Charry, Oscar Julianc280ba13fd48e8dbf9cdbc8179aa9c94Sánchez Legarda, Yeison David4b2487ffd7d0b98eed416dc02b2c2975600Mindlab2022-03-03T16:30:51Z2022-03-03T16:30:51Z2021https://repositorio.unal.edu.co/handle/unal/81123Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasThe most common causes of blindness around the world are retinal diseases, to identify them and not allow to lead to loss of vision for a person an early diagnosis is necessary, nowadays the use of OCT scans to perform this diagnostic has increased due to the capacity to show in detail biomarkers as fluids, drusen, cyst and hyperreflective foci. However the OCT scans analysis is not easy and time consuming even for experts ophthalmologist and in combination with the overload work overload in the healthcare system makes even more difficult to diagnose and follow-up the retinal disease, at this point comes in to help deep learning allowing the automated detection of diseases and biomarkers, With the thesis work “Deep Learning Approach to Identify Diseases and Biomarkers in Optical Coherence Tomography Scans," a method was proposed to OCT scans segmentation to obtain biomarkers which can help the ophthalmologist to check response to treatment or identify a retinal disease, furthermore a deep learning method for check which disease is present in a scan was implemented.Las causas más comunes de ceguera en todo el mundo son las enfermedades de la retina, para identificarlas y no permitir que lleven a la pérdida de la visión de una persona es necesario un diagnóstico temprano, hoy en día el uso de las imágenes OCT para realizar este diagnóstico se ha incrementado debido a la capacidad de mostrar en detalle biomarcadores como fluidos, drusas, quistes y focos hiperreflectivos, sin embargo el análisis de las imágenes OCT no es fácil y consume mucho tiempo incluso para los oftalmólogos expertos lo que combinado con la sobrecarga de trabajo en el sistema de salud hace aún más difícil el diagnóstico y seguimiento de las enfermedades retinales, Con el trabajo de tesis "Deep Learning Approach to Identify Diseases and Biomarkers in Optical Coherence Tomography Scans", se propone un método para la segmentación de imágenes OCT con el fin de obtener biomarcadores que puedan ayudar al oftalmólogo a comprobar la respuesta al tratamiento o identificar una enfermedad de la retina, además se implementó un método de aprendizaje profundo para comprobar qué enfermedad está presente en una imagen. (Texto tomado de la fuente).MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónDeep learningComputer visionMachine learningxii, 56 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 Bogotá000 - Ciencias de la computación, información y obras generalesDeep LearningRetinal DiseasesTomography, OpticalAprendizaje profundoEnfermedades de la RetinaTomografía ÓpticaComputer visionDeep learningMachine learningOptical coherence tomography scansBiomarkers segmentationRetinal diseases classificationAprendizaje profundoTomografía de coherencia ópticaVisión por computadorAprendizaje de máquinasSegmentación de biomarcadoresClasificación de enfermedades retinianasRedes neuronales generativas adversariasDeep learning approach to identify diseases and biomarkers in optical coherence tomography scansEnfoque de aprendizaje profundo para identificar enfermedades y biomarcadores en imagenes de tomografía de coherencia ópticaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMJ D Moura, J Novo, and M Ortega. 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In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018EstudiantesInvestigadoresMaestrosORIGINAL1104704188.2021.pdf1104704188.2021.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf3175576https://repositorio.unal.edu.co/bitstream/unal/81123/1/1104704188.2021.pdf6864d8c419de22945b88345ef37a59b0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81123/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1104704188.2021.pdf.jpg1104704188.2021.pdf.jpgGenerated Thumbnailimage/jpeg4440https://repositorio.unal.edu.co/bitstream/unal/81123/3/1104704188.2021.pdf.jpg5315a673cf8832b80518a8b4484099b0MD53unal/81123oai:repositorio.unal.edu.co:unal/811232024-08-03 23:10:51.333Repositorio Institucional Universidad Nacional de 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