Automatic detection of colorectal polyps larger than 5 mm in colonoscopy videos

dc.contributorRomero Castro, Edgar Eduardo
dc.contributorUniversidad Nacional de Colombia
dc.contributorCIM@LAB (Computer Imaging and Medical Applications Laboratory)
dc.creatorBravo Higuera, Diego Fernando
dc.date.accessioned2020-08-13T03:21:19Z
dc.date.available2020-08-13T03:21:19Z
dc.date.created2020-08-13T03:21:19Z
dc.date.issued2020-01-06
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/78013
dc.description.abstractEl cáncer colorrectal (CCR) fue la segunda causa más común de muerte por cáncer en el mundo en 2018 y se ha convertido en una prioridad de salud pública mundial. Por lo tanto, la prevención de CCR mediante la detección temprana y la eliminación de lesiones neoplásicas es de suma importancia. Por lo general, el CCR comienza con pequeñas masas benignas o neoplasias comúnmente llamadas pólipos. En la mayoría de los casos, los pólipos evolucionan lentamente en adenocarcinoma o cáncer. La colonoscopia es el examen estándar para diagnosticar y tratar el CCR. Durante este procedimiento, un gastroenterólogo realiza una exploración visual de todo el colon para detectar esas lesiones y definir una actitud terapéutica. Sin embargo, algunos estudios de población a gran escala han informado que aproximadamente el 25% de los pólipos no son detectados durante la colonoscopia. Los pacientes con una tasa de omisión de pólipos pueden desarrollar CCR y en una etapa tardía, la tasa de supervivencia es inferior al 15%. La detección de pólipos es una tarea compleja que depende en gran medida de la experiencia del especialista y fatiga ocular, preparación intestinal del paciente y la variación biológica. Por lo tanto, un sistema automático de detección de pólipos como segundo lector puede ayudar a reducir la tasa de omisión de pólipos, resaltando las posibles regiones polipoides para aumentar la atención de los expertos. Este trabajo presenta un conjunto de métodos automáticos para apoyar el diagnóstico médico, inspirados en las características visuales, información contextual y temporal de las lesiones polipoides mayores de 5 milímetros, esta descripción permite la diferenciación de la clase de pólipo y no pólipo.
dc.description.abstractColorectal cancer (CRC) was the second most common cause of cancer death in the world in 2018 and it has become a global public health priority. Therefore, the prevention of CRC through the early detection and elimination of neoplastic lesions is of paramount importance. Usually, the CRC begins as small benign masses or neoplasias commonly called polyps. In most cases, polyps slowly evolve in adenocarcinoma or cancer. Colonoscopy is the standard test to diagnose and treat CRC. During this procedure, a visual examination of the entire colon is performed by a gastroenterologist detecting those lesions and defining a therapeutic attitude. However, some large-scale population studies have reported that approximately 25% of polyps are missed during colonoscopy exploration. The patients with a miss-rate of polyps can develop CRC and at a late stage, the survival rate is less than 15%. Polyp detection is a complex task being highly dependent on the specialist experience and fatigue, bowel preparation of the patient, and biological variation. Therefore, an automatic polyp detection system as a second reader could help to reduce the polyp miss rate, highlighting possible polypoid regions to increase the attention of experts. This work presents a set of automatic methods to support medical diagnosis, inspired in the visual features, contextual and temporal information of polypoid lesions larger than 5 millimeters, this description allows the differentiation of the polyp and non-polyp class.
dc.languageeng
dc.publisherBogotá - Medicina - Maestría en Ingeniería Biomédica
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-SinDerivadas 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleAutomatic detection of colorectal polyps larger than 5 mm in colonoscopy videos
dc.typeOtro


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