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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.date.accessioned2020-11-05T21:13:05Z
dc.date.available2020-11-05T21:13:05Z
dc.date.issued2020-08
dc.date.submitted2020-11-03
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); https://doi.org/10.1117/12.2569258spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9561
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 cornea.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
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
dcterms.bibliographicCitationGalvis, V., Tello, A., Delgado, J., Guti´errez, A., Rodr´ıguez, L., and Chaparro, T., “Reproducibilidad de los resultados del an´alisis endothelial con el microscopio especular de no contacto topcon sp-3000p,” Revista Sociedad Colombiana de Oftalmolog´ıa 44(3), 253–260 (2011).spa
dcterms.bibliographicCitationGalvis, V., Tello, A., and Gutierrez, A. J., “Human corneal endothelium regeneration: effect of rock in- ´ hibitor,” Investigative ophthalmology & visual science 54(7), 4971–4973 (2013).spa
dcterms.bibliographicCitationGalvis, V., Tello, A., Carre˜no, N. I., Berrospi, R. D., and Ni˜no, C. A., “Potential use of thermoreversible hydrogel (poloxamer 407) to protect the corneal endothelium and the posterior capsule during phacoemulsification,” Journal of Cataract & Refractive Surgery 45(3), 389 (2019).spa
dcterms.bibliographicCitationGalvis, V., Villamil, J. F., Acu˜na, M. F., Camacho, P. A., Merayo-Lloves, J., Tello, A., Zambrano, S. L., Rey, J. J., Espinoza, J. V., and Prada, A. M., “Long-term endothelial cell loss with the iris-claw intraocular phakic lenses (artisan R ),” Graefe’s Archive for Clinical and Experimental Ophthalmology 257(12), 2775– 2787 (2019).spa
dcterms.bibliographicCitationFeizi, S., “Corneal endothelial cell dysfunction: etiologies and management,” Therapeutic Advances in Ophthalmology 10, 2515841418815802 (2018).spa
dcterms.bibliographicCitationEghrari, A. O., Riazuddin, S. A., and Gottsch, J. D., “Fuchs corneal dystrophy,” in [Progress in molecular biology and translational science], 134, 79–97, Elsevier (2015).spa
dcterms.bibliographicCitationGalvis, V., Tello, A., Gomez, A. J., Rangel, C. M., Prada, A. M., and Camacho, P. A., “Corneal transplantation at an ophthalmological referral center in colombia: indications and techniques (2004-2011),” The open ophthalmology journal 7, 30 (2013)spa
dcterms.bibliographicCitationGalvis, V., Tello, A., Laiton, A. N., and Salcedo, S. L., “Indications and techniques of corneal transplantation in a referral center in colombia, south america (2012–2016),” International ophthalmology 39(8), 1723–1733 (2019)spa
dcterms.bibliographicCitationLaing, R. A., Leibowitz, H. M., Oak, S. S., Chang, R., Berrospi, A. R., and Theodore, J., “Endothelial mosaic in fuchs’ dystrophy: A qualitative evaluation with the specular microscope,” Archives of Ophthalmology 99(1), 80–83 (1981).spa
dcterms.bibliographicCitationSelig, B., Vermeer, K. A., Rieger, B., Hillenaar, T., and Hendriks, C. L. L., “Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy,” BMC medical imaging 15(1), 13 (2015).spa
dcterms.bibliographicCitationAl-Fahdawi, S., Qahwaji, R., Al-Waisy, A. S., Ipson, S., Ferdousi, M., Malik, R. A., and Brahma, A., “A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology,” Computer methods and programs in biomedicine 160, 11–23 (2018).spa
dcterms.bibliographicCitationScarpa, F. and Ruggeri, A., “Automated morphometric description of human corneal endothelium from in-vivo specular and confocal microscopy,” in [2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ], 1296–1299, IEEE (2016).spa
dcterms.bibliographicCitationScarpa, F. and Ruggeri, A., “Development of a reliable automated algorithm for the morphometric analysis of human corneal endothelium,” Cornea 35(9), 1222–1228 (2016).spa
dcterms.bibliographicCitationGiasson, C. J., Graham, A., Blouin, J.-F., Solomon, L., Gresset, J., Melillo, M., and Polse, K. A., “Morphometry of cells and guttae in subjects with normal or guttate endothelium with a contour detection algorithm,” Eye & Contact Lens 31(4), 158–165 (2005).spa
dcterms.bibliographicCitation] Ruggeri, A., Scarpa, F., De Luca, M., Meltendorf, C., and Schroeter, J., “A system for the automatic estimation of morphometric parameters of corneal endothelium in alizarine red-stained images,” British Journal of Ophthalmology 94(5), 643–647 (2010).spa
dcterms.bibliographicCitationNurzynska, K., “Deep learning as a tool for automatic segmentation of corneal endothelium images,” Symmetry 10(3), 60 (2018).spa
dcterms.bibliographicCitationVigueras-Guill´en, J. P., Sari, B., Goes, S. F., Lemij, H. G., van Rooij, J., Vermeer, K. A., and van Vliet, L. J., “Fully convolutional architecture vs sliding-window cnn for corneal endothelium cell segmentation,” BMC Biomedical Engineering 1(1), 1–16 (2019).spa
dcterms.bibliographicCitationDaniel, M. C., Atzrodt, L., Bucher, F., Wacker, K., B¨ohringer, S., Reinhard, T., and B¨ohringer, D., “Automated segmentation of the corneal endothelium in a large set of ‘real-world’specular microscopy images using the u-net architecture,” Scientific reports 9(1), 1–7 (2019).spa
dcterms.bibliographicCitationRonneberger, O., Fischer, P., and Brox, T., “U-net: Convolutional networks for biomedical image segmentation,” in [International Conference on Medical image computing and computer-assisted intervention], 234–241, Springer (2015)spa
dcterms.bibliographicCitationFalk, T., Mai, D., Bensch, R., C¸ i¸cek, O., Abdulkadir, A., Marrakchi, Y., B¨ohm, A., Deubner, J., J¨ackel, ¨ Z., Seiwald, K., et al., “U-net: deep learning for cell counting, detection, and morphometry,” Nature methods 16(1), 67–70 (2019).spa
dcterms.bibliographicCitationCarton, F.-X., Chabanas, M., Le Lann, F., and Noble, J. H., “Automatic segmentation of brain tumor resections in intraoperative ultrasound images using u-net,” Journal of Medical Imaging 7(3), 031503 (2020).spa
dcterms.bibliographicCitationSierra, J. S., Pineda, J., Viteri, E., Tello, A., Mill´an, M. S., Galvis, V., Romero, L. A., and Marrugo, A. G., “Generating density maps for convolutional neural network-based cell counting in specular microscopy images,” Journal of Physics: Conf. Series 1547(1), 012019 (2020).spa
dcterms.bibliographicCitationOunkomol, C., Seshamani, S., Maleckar, M. M., Collman, F., and Johnson, G. R., “Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy,” Nature methods 15(11), 917–920 (2018).spa
dcterms.bibliographicCitationIoffe, S. and Szegedy, C., “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 (2015)spa
dcterms.bibliographicCitationKingma, D. P. and Ba, J., “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).spa
dcterms.bibliographicCitation] Helgadottir, S., Argun, A., and Volpe, G., “Digital video microscopy enhanced by deep learning,” Optica 6(4), 506–513 (2019).spa
dcterms.bibliographicCitationMidtvedt, B., Helgadottir, S., Argun, A., Midtvedt, D., and Volpe, G., “Deeptrack: A comprehensive deep learning framework for digital microscopy.” https://github.com/softmatterlab/DeepTrack-2.0. git (2020).spa
datacite.rightshttp://purl.org/coar/access_right/c_14cbspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.identifier.urlhttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/11511/115110H/Automated-corneal-endothelium-image-segmentation-in-the-presence-of-cornea/10.1117/12.2569258.short?SSO=1
dc.type.driverinfo:eu-repo/semantics/lecturespa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.doi10.1117/12.2569258
dc.subject.keywordsCell segmentationspa
dc.subject.keywordsOphthalmic imagingspa
dc.subject.keywordsDiagnosis through imagesspa
dc.subject.keywordsImage processingspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.type.spahttp://purl.org/coar/resource_type/c_c94fspa
dc.audiencePúblico generalspa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_c94fspa


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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.