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dc.contributor.authorSierra, Juan S.
dc.contributor.authorPineda, Jesus
dc.contributor.authorViteri, Eduardo
dc.contributor.authorRueda, Daniela
dc.contributor.authorTibaduiza, Beatriz
dc.contributor.authorBerrospi, Rúben D.
dc.contributor.authorTello, Alejandro
dc.contributor.authorGalvis, Virgilio
dc.contributor.authorVolpe, Giovanni
dc.contributor.authorMillán, María S.
dc.contributor.authorRomero, Lenny A.
dc.contributor.authorMarrugo, Andrés G.
dc.identifier.citationJuan S. Sierra, Jesus Pineda, Eduardo Viteri, Daniela Rueda, Beatriz Tibaduiza, Rúben D. Berrospi, Alejandro Tello, Virgilio Galvis, Giovanni Volpe, María S. Millán, Lenny A. Romero, and Andrés G. Marrugo "Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110H (19 August 2020);
dc.description.abstractAutomated cell counting in in-vivo specular microscopy images is challenging, especially in situations where single-cell segmentation methods fail due to pathological conditions. This work aims to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. We cast the problem of cell segmentation as a supervised multi-class segmentation problem. The goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, indicating healthy (cells) and pathological regions (e.g., guttae). We trained a U-net model by extracting 96×96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a physician. Encouraging results show that the proposed method can deliver reliable feature segmentation enabling more accurate cell density estimations for assessing the state of the
dc.sourceProceedings Volume 11511, Applications of Machine Learning 2020; 115110H (2020)spa
dc.titleAutomated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networksspa
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dc.subject.keywordsCell segmentationspa
dc.subject.keywordsOphthalmic imagingspa
dc.subject.keywordsDiagnosis through imagesspa
dc.subject.keywordsImage processingspa
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.audiencePúblico generalspa

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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.