Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessme...

Full description

Autores:
Sierra, Juan S.
Pineda, Jesus
Rueda, Daniela
Tello, Alejandro
Prada, Angélica M.
Galvis, Virgilio
Volpe, Giovanni
Millan, Maria S.
Romero, Lenny A.
Marrugo, Andres G.
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12342
Acceso en línea:
https://hdl.handle.net/20.500.12585/12342
Palabra clave:
Corneal Endothelium;
Hexagonal Cells;
Capillary Endothelial Cell
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_777f66ca3ef635e6ca17bb0ffeb00f22
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/12342
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
title Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
spellingShingle Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
Corneal Endothelium;
Hexagonal Cells;
Capillary Endothelial Cell
LEMB
title_short Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
title_full Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
title_fullStr Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
title_full_unstemmed Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
title_sort Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
dc.creator.fl_str_mv Sierra, Juan S.
Pineda, Jesus
Rueda, Daniela
Tello, Alejandro
Prada, Angélica M.
Galvis, Virgilio
Volpe, Giovanni
Millan, Maria S.
Romero, Lenny A.
Marrugo, Andres G.
dc.contributor.author.none.fl_str_mv Sierra, Juan S.
Pineda, Jesus
Rueda, Daniela
Tello, Alejandro
Prada, Angélica M.
Galvis, Virgilio
Volpe, Giovanni
Millan, Maria S.
Romero, Lenny A.
Marrugo, Andres G.
dc.subject.keywords.spa.fl_str_mv Corneal Endothelium;
Hexagonal Cells;
Capillary Endothelial Cell
topic Corneal Endothelium;
Hexagonal Cells;
Capillary Endothelial Cell
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs’ dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 µm2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-21T16:24:39Z
dc.date.available.none.fl_str_mv 2023-07-21T16:24:39Z
dc.date.issued.none.fl_str_mv 2023
dc.date.submitted.none.fl_str_mv 2023
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/draft
dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
status_str draft
dc.identifier.citation.spa.fl_str_mv Sierra, J. S., Pineda, J., Rueda, D., Tello, A., Prada, A. M., Galvis, V., ... & Marrugo, A. G. (2023). Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps. Biomedical optics express, 14(1), 335-351.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12342
dc.identifier.doi.none.fl_str_mv 10.1364/BOE.477495
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Sierra, J. S., Pineda, J., Rueda, D., Tello, A., Prada, A. M., Galvis, V., ... & Marrugo, A. G. (2023). Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps. Biomedical optics express, 14(1), 335-351.
10.1364/BOE.477495
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12342
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 17 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Biomedical Optics Express
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/1/boe-14-1-335.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/4/boe-14-1-335.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/5/boe-14-1-335.pdf.jpg
bitstream.checksum.fl_str_mv 5a90e4a111904debb613d4c91c5aaa2c
4460e5956bc1d1639be9ae6146a50347
e20ad307a1c5f3f25af9304a7a7c86b6
a94e19c54690307db1154d095f14b9b9
cd1927e73a13d3500c8d2d8de3dc5618
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1814021571043393536
spelling Sierra, Juan S.86e96289-2908-4813-8796-f9a7e5cbcdf0Pineda, Jesusa6827c4e-c14f-4dc1-ba8e-4c5b1cd055ebRueda, Danielaa16eb811-7abb-4b60-93f2-69405e0136f0Tello, Alejandrob88c245a-e5d9-4feb-a8e5-fc2a6555415dPrada, Angélica M.5b17ebe8-d90b-4931-940c-55a68e348906Galvis, Virgilio85e1c5d8-b4a4-4bed-828d-267cd8ca4b5bVolpe, Giovanni84cb6735-e854-4db6-af72-f7a4df329847Millan, Maria S.7079d596-8c24-45f6-bce3-29ab50b77e5cRomero, Lenny A.4e34aa8a-f981-4e1d-ae32-d45acb6abcf9Marrugo, Andres G.3d6cd388-d48f-4669-934f-49ca4179f5422023-07-21T16:24:39Z2023-07-21T16:24:39Z20232023Sierra, J. S., Pineda, J., Rueda, D., Tello, A., Prada, A. M., Galvis, V., ... & Marrugo, A. G. (2023). Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps. Biomedical optics express, 14(1), 335-351.https://hdl.handle.net/20.500.12585/1234210.1364/BOE.477495Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSpecular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs’ dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 µm2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.17 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Biomedical Optics ExpressCorneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance mapsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Corneal Endothelium;Hexagonal Cells;Capillary Endothelial CellLEMBCartagena de IndiasGiasson, C.J., Graham, A., Blouin, J.-F., Solomon, L., Gresset, J., Melillo, M., Polse, K.A. Morphometry of cells and guttae in subjects with normal or guttate endothelium with a contour detection algorithm (2005) Eye and Contact Lens, 31 (4), pp. 158-165. Cited 11 times. doi: 10.1097/01.ICL.0000165286.05080.23Sierra, J.S., Pineda, J., Viteri, E., Rueda, D., Tibaduiza, B., Berrospi, R.D., Tello, A., (...), Marrugo, A.G. Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks (2020) Proceedings of SPIE - The International Society for Optical Engineering, 11511, art. no. 115110H. Cited 6 times. http://spie.org/x1848.xml ISBN: 978-151063828-0 doi: 10.1117/12.2569258Selig, B., Vermeer, K.A., Rieger, B., Hillenaar, T., Luengo Hendriks, C.L. Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy (2015) BMC Medical Imaging, 15 (1), art. no. 13. Cited 41 times. http://www.biomedcentral.com/bmcmedimaging/ doi: 10.1186/s12880-015-0054-3Ong Tone, S., Jurkunas, U. Imaging the Corneal Endothelium in Fuchs Corneal Endothelial Dystrophy (2019) Seminars in Ophthalmology, 34 (4), pp. 340-346. Cited 16 times. http://www.tandfonline.com/loi/isio20 doi: 10.1080/08820538.2019.1632355Yasukura, Y., Oie, Y., Kawasaki, R., Maeda, N., Jhanji, V., Nishida, K. New severity grading system for Fuchs endothelial corneal dystrophy using anterior segment optical coherence tomography (2021) Acta Ophthalmologica, 99 (6), pp. e914-e921. Cited 5 times. http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1755-3768 doi: 10.1111/aos.14690Laing, R.A., Sandstrom, M.M., Leibowitz, H.M. Clinical specular microscopy. I. Optical principles (1979) Archives of Ophthalmology, 97 (9), pp. 1714-1719. Cited 50 times. doi: 10.1001/archopht.1979.01020020282021Srinivasan, M. Chapter-22 specular microscopy (2005) Modern Ophthalmology, pp. 147-153. (Jaypee Brothers Medical Publishers (P) Ltd)Nurzynska, K. Deep learning as a tool for automatic segmentation of corneal endothelium images (2018) Symmetry, 10 (3), art. no. 60. Cited 24 times. https://res.mdpi.com/symmetry/symmetry-10-00060/article_deploy/symmetry-10-00060.pdf?filename=&attachment=1 doi: 10.3390/SYM10030060Fabijańska, A. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network (2018) Artificial Intelligence in Medicine, 88, pp. 1-13. Cited 66 times. www.elsevier.com/locate/artmed doi: 10.1016/j.artmed.2018.04.004Scarpa, F., Ruggeri, A. Automated morphometric description of human corneal endothelium from in-vivo specular and confocal microscopy (2016) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016-October, art. no. 7590944, pp. 1296-1299. Cited 13 times. ISBN: 978-145770220-4 doi: 10.1109/EMBC.2016.7590944Scarpa, F., Ruggeri, A. Development of a reliable automated algorithm for the morphometric analysis of human corneal endothelium (2016) Cornea, 35 (9), pp. 1222-1228. Cited 25 times. http://journals.lww.com/corneajrnl/pages/default.aspx doi: 10.1097/ICO.0000000000000908Sanchez-Marin, F.J. Automatic segmentation of contours of corneal cells (1999) Computers in Biology and Medicine, 29 (4), pp. 243-258. Cited 35 times. doi: 10.1016/S0010-4825(99)00010-4Al-Fahdawi, S., Qahwaji, R., Al-Waisy, A.S., Ipson, S., Ferdousi, M., Malik, R.A., Brahma, A. A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology (2018) Computer Methods and Programs in Biomedicine, 160, pp. 11-23. Cited 27 times. www.elsevier.com/locate/cmpb doi: 10.1016/j.cmpb.2018.03.015Piorkowski, A., Nurzynska, K., Gronkowska-Serafin, J., Selig, B., Boldak, C., Reska, D. Influence of applied corneal endothelium image segmentation techniques on the clinical parameters (2017) Computerized Medical Imaging and Graphics, 55, pp. 13-27. Cited 27 times. www.elsevier.com/locate/compmedimag doi: 10.1016/j.compmedimag.2016.07.010Ruggeri, A., Grisan, E., Jaroszewski, J. A new system for the automatic estimation of endothelial cell density in donor corneas (2005) British Journal of Ophthalmology, 89 (3), pp. 306-311. Cited 40 times. doi: 10.1136/bjo.2004.051722Vicar, T., Chmelik, J., Jakubicek, R., Chmelikova, L., Gumulec, J., Balvan, J., Provaznik, I., (...), Kolar, R. Self-supervised pretraining for transferable quantitative phase image cell segmentation (Open Access) (2021) Biomedical Optics Express, 12 (10), pp. 6514-6528. Cited 2 times. https://www.osapublishing.org/abstract.cfm?uri=boe-12-10-6514 doi: 10.1364/BOE.433212Daniel, M.C., Atzrodt, L., Bucher, F., Wacker, K., Böhringer, S., Reinhard, T., Böhringer, D. Automated segmentation of the corneal endothelium in a large set of ‘real-world’ specular microscopy images using the U-Net architecture (Open Access) (2019) Scientific Reports, 9 (1), art. no. 4752. Cited 29 times. www.nature.com/srep/index.html doi: 10.1038/s41598-019-41034-2Ronneberger, O., Fischer, P., Brox, T. U-net: Convolutional networks for biomedical image segmentation (2015) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, pp. 234-241. Cited 37884 times. http://springerlink.com/content/0302-9743/copyright/2005/ ISBN: 978-331924573-7 doi: 10.1007/978-3-319-24574-4_28Joseph, N., Kolluru, C., Benetz, B.A.M., Menegay, H.J., Lass, J.H., Wilson, D.L. Quantitative and qualitative evaluation of deep learning automatic segmentations of corneal endothelial cell images of reduced image quality obtained following cornea transplant (Open Access) (2020) Journal of Medical Imaging, 7 (1), art. no. 014503. Cited 18 times. http://medicalimaging.spiedigitallibrary.org/journal.aspx doi: 10.1117/1.JMI.7.1.014503Vigueras-Guillén, J. P., Sari, B., Goes, S. F., Lemij, H. G., van Rooij, J., Vermeer, K. A., van Vliet, L. J. Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation (2019) BMC Biomed. Eng, 1, p. 4. Cited 39 times.Vigueras-Guillén, J.P., van Rooij, J., Engel, A., Lemij, H.G., van Vliet, L.J., Vermeer, K.A. Deep learning for assessing the corneal endothelium from specular microscopy images up to 1 year after ultrathin-dsaek surgery (2020) Translational Vision Science and Technology, 9 (2), art. no. 49, pp. 1-12. Cited 15 times. https://tvst.arvojournals.org/article.aspx?articleid=2770688 doi: 10.1167/tvst.9.2.49Vigueras-Guillén, J.P., Lemij, H.G., Van Rooij, J., Vermeer, K.A., Van Vliet, L.J. Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks (2019) Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 10949, art. no. 1094931. Cited 6 times. http://spie.org/x1848.xml ISBN: 978-151062545-7 doi: 10.1117/12.2512641Vigueras-Guillen, J.P., Van Rooij, J., Lemij, H.G., Vermeer, K.A., Van Vliet, L.J. Convolutional neural network-based regression for biomarker estimation in corneal endothelium microscopy images (Open Access) (2019) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 8857201, pp. 876-881. Cited 8 times. ISBN: 978-153861311-5 doi: 10.1109/EMBC.2019.8857201Vigueras-Guillén, J.P., van Rooij, J., van Dooren, B.T.H., Lemij, H.G., Islamaj, E., van Vliet, L.J., Vermeer, K.A. DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae (Open Access) (2022) Scientific Reports, 12 (1), art. no. 14035. Cited 3 times. www.nature.com/srep/index.html doi: 10.1038/s41598-022-18180-1Aiello, F., Gallo Afflitto, G., Ceccarelli, F., Cesareo, M., Nucci, C. Global Prevalence of Fuchs Endothelial Corneal Dystrophy (FECD) in Adult Population: A Systematic Review and Meta-Analysis (2022) Journal of Ophthalmology, 2022, art. no. 3091695. Cited 9 times. http://www.hindawi.com/journals/jop/ doi: 10.1155/2022/3091695Feizi, S. Corneal endothelial cell dysfunction: etiologies and management (2018) Therapeutic Advances in Ophthalmology, 10, p. 251584141881580. Cited 71 times.Eghrari, A.O., Riazuddin, S.A., Gottsch, J.D. Fuchs Corneal Dystrophy (Open Access) (2015) Progress in Molecular Biology and Translational Science, 134, pp. 79-97. Cited 57 times. http://www.elsevier.com/books/book-series/progress-in-molecular-biology-and-translational-science# ISBN: 978-012801059-4 doi: 10.1016/bs.pmbts.2015.04.005Laing, R.A., Leibowitz, H.M., Oak, S.S., Chang, R., Berrospi, A.R., Theodore, J. Endothelial Mosaic in Fuchs' Dystrophy: A Qualitative Evaluation With the Specular Microscope (Open Access) (1981) Archives of Ophthalmology, 99 (1), pp. 80-83. Cited 53 times. doi: 10.1001/archopht.1981.03930010082007Chiou, A.G.-Y., Kaufman, S.C., Beuerman, R.W., Ohta, T., Soliman, H., Kaufman, H.E. Confocal microscopy in cornea guttata and Fuchs' endothelial dystrophy (Open Access) (1999) British Journal of Ophthalmology, 83 (2), pp. 185-189. Cited 80 times. http://bjo.bmj.com/ doi: 10.1136/bjo.83.2.185Hogan, M.J., Wood, I., Fine, M. Fuchs' endothelial dystrophy of the cornea. 29th Sanford Gifford Memorial Lecture (1974) American Journal of Ophthalmology, 78 (3), pp. 363-383. Cited 84 times. doi: 10.1016/0002-9394(74)90224-4Iwamoto, T., DeVoe, A.G. Electron microscopic studies on Fuchs'combined dystrophy. I. Posterior portion of the cornea. (Open Access) (1971) Investigative ophthalmology, 10 (1), pp. 9-28. Cited 106 times.Ong Tone, S., Kocaba, V., Böhm, M., Wylegala, A., White, T.L., Jurkunas, U.V. Fuchs endothelial corneal dystrophy: The vicious cycle of Fuchs pathogenesis (Open Access) (2021) Progress in Retinal and Eye Research, 80, art. no. 100863. Cited 50 times. www.elsevier.com/locate/preteyeres doi: 10.1016/j.preteyeres.2020.100863He, S., Minn, K.T., Solnica-Krezel, L., Anastasio, M.A., Li, H. Deeply-supervised density regression for automatic cell counting in microscopy images (Open Access) (2021) Medical Image Analysis, 68, art. no. 101892. Cited 24 times. http://www.elsevier.com/inca/publications/store/6/2/0/9/8/3/index.htt doi: 10.1016/j.media.2020.101892Sierra, J.S., Pineda, J., Viteri, E., Tello, A., Millán, M.S., Galvis, V., Romero, L.A., (...), Marrugo, A.G. Generating density maps for convolutional neural network-based cell counting in specular microscopy images (Open Access) (2020) Journal of Physics: Conference Series, 1547 (1), art. no. 012019. Cited 6 times. http://iopscience.iop.org/journal/1742-6596 doi: 10.1088/1742-6596/1547/1/012019Lundh, F. (1999) An introduction to tkinter. Cited 65 times. www.pythonware.com/library/tkinter/introduction/index.htmGrauer, J., Schmidt, F., Pineda, J., Midtvedt, B., Löwen, H., Volpe, G., Liebchen, B. Active droploids (Open Access) (2021) Nature Communications, 12 (1), art. no. 6005. Cited 7 times. http://www.nature.com/ncomms/index.html doi: 10.1038/s41467-021-26319-3Naylor, P., Laé, M., Reyal, F., Walter, T. Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map (Open Access) (2019) IEEE Transactions on Medical Imaging, 38 (2), art. no. 8438559, pp. 448-459. Cited 281 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=42 doi: 10.1109/TMI.2018.2865709Ulyanov, D., Vedaldi, A., Lempitsky, V. (2016) Instance normalization: The missing ingredient for fast stylizationmap. Cited 1805 times. arXiv, arXiv:1607.08022Maas, A. L., Hannun, A. Y., Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models (2013) International conference on machine learning, 30. Cited 4516 times.Xu, B., Wang, N., Chen, T., Li, M. (2015) Empirical evaluation of rectified activations in convolutional network. Cited 1522 times. arXiv, arXiv:1505.00853He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition (2016) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, art. no. 7780459, pp. 770-778. Cited 108313 times. ISBN: 978-146738850-4 doi: 10.1109/CVPR.2016.90Helgadottir, S., Midtvedt, B., Pineda, J., Sabirsh, A., Adiels, C. B., Romeo, S., Midtvedt, D., (...), Volpe, G. Extracting quantitative biological information from bright-field cell images using deep learning (2021) Biophysics Rev, 2 (3), p. 031401. Cited 7 times.Midtvedt, B., Helgadottir, S., Argun, A., Pineda, J., Midtvedt, D., Volpe, G. Quantitative digital microscopy with deep learning (Open Access) (2021) Applied Physics Reviews, 8 (1), art. no. 011310. Cited 35 times. http://scitation.aip.org/content/aip/journal/apr2/browse doi: 10.1063/5.0034891Helgadottir, S., Argun, A., Volpe, G. Digital video microscopy enhanced by deep learning (Open Access) (2019) Optica, 6 (4), pp. 506-513. Cited 42 times. https://www.osapublishing.org/optica/viewmedia.cfm?uri=optica-6-4-506&seq=0 doi: 10.1364/OPTICA.6.000506Kingma, D. P., Ba, J. (2014) Adam: A method for stochastic optimization. Cited 51822 times. arXiv, arXiv:1412.6980Roszkowska, A.M., Colosi, P., D'Angelo, P., Ferreri, G. Age-related modifications of the corneal endothelium in adults (2004) International Ophthalmology, 25 (3), pp. 163-166. Cited 34 times. www.wkap.nl/journalhome.htm/0165-5701 doi: 10.1007/s10792-004-1957-0Valdez-García, J.E., Ortiz-Morales, G., Morales-Mancillas, N., Domenehickman, J.L., Hernández-Camarena, J., Loya-García, D., Zavala, J., (...), Rodriguez-García, A. Age-related Changes of the Corneal Endothelium in the Hispanic Elderly Population (Open Access) (2022) Open Ophthalmology Journal, 16 (1), art. no. e187436412204140. Cited 2 times. https://www.benthamopen.com/TOOPHTJ/ doi: 10.2174/18743641-v16-e2204140Kudva, A., Lasrado, A., Hegde, S., Kadri, R., Devika, P., Shetty, A. Corneal endothelial cell changes in diabetics versus age group matched nondiabetics after manual small incision cataract surgery (Open Access) (2020) Indian Journal of Ophthalmology, 68 (1), pp. 72-76. Cited 10 times. http://www.ijo.in/ doi: 10.4103/ijo.IJO_406_19Shilpashree, P.S., Suresh, K.V., Sudhir, R.R., Srinivas, S.P. Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy (Open Access) (2021) Translational Vision Science and Technology, 10 (13), art. no. 27. Cited 6 times. https://tvst.arvojournals.org/ doi: 10.1167/tvst.10.13.27Ahmed, I., Balestrieri, E., Carni, D.L., Lamonaca, F. Comparison of U -NET backbones for morphometric measurements of white blood cell (Open Access) (2022) 2022 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2022 - Conference Proceedings http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9855895 ISBN: 978-166548299-8 doi: 10.1109/MeMeA54994.2022.9856479Yeghiazaryan, V., Voiculescu, I. Family of boundary overlap metrics for the evaluation of medical image segmentation (Open Access) (2018) Journal of Medical Imaging, 5 (1), art. no. 015006. Cited 88 times. http://medicalimaging.spiedigitallibrary.org/journal.aspx doi: 10.1117/1.JMI.5.1.015006Sierra, J., Pineda, J., Volpe, G., Romero, L. A., Marrugo, A. G. Code for corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps (2022) GitHub https://doi.org/10.5281/zenodo.7378507http://purl.org/coar/resource_type/c_6501ORIGINALboe-14-1-335.pdfboe-14-1-335.pdfapplication/pdf10472657https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/1/boe-14-1-335.pdf5a90e4a111904debb613d4c91c5aaa2cMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTboe-14-1-335.pdf.txtboe-14-1-335.pdf.txtExtracted texttext/plain53842https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/4/boe-14-1-335.pdf.txta94e19c54690307db1154d095f14b9b9MD54THUMBNAILboe-14-1-335.pdf.jpgboe-14-1-335.pdf.jpgGenerated Thumbnailimage/jpeg7810https://repositorio.utb.edu.co/bitstream/20.500.12585/12342/5/boe-14-1-335.pdf.jpgcd1927e73a13d3500c8d2d8de3dc5618MD5520.500.12585/12342oai:repositorio.utb.edu.co:20.500.12585/123422023-07-22 00:17:44.344Repositorio Institucional UTBrepositorioutb@utb.edu.coQXV0b3Jpem8gKGF1dG9yaXphbW9zKSBhIGxhIEJpYmxpb3RlY2EgZGUgbGEgSW5zdGl0dWNpw7NuIHBhcmEgcXVlIGluY2x1eWEgdW5hIGNvcGlhLCBpbmRleGUgeSBkaXZ1bGd1ZSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsLCBsYSBvYnJhIG1lbmNpb25hZGEgY29uIGVsIGZpbiBkZSBmYWNpbGl0YXIgbG9zIHByb2Nlc29zIGRlIHZpc2liaWxpZGFkIGUgaW1wYWN0byBkZSBsYSBtaXNtYSwgY29uZm9ybWUgYSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBxdWUgbWUobm9zKSBjb3JyZXNwb25kZShuKSB5IHF1ZSBpbmNsdXllbjogbGEgcmVwcm9kdWNjacOzbiwgY29tdW5pY2FjacOzbiBww7pibGljYSwgZGlzdHJpYnVjacOzbiBhbCBww7pibGljbywgdHJhbnNmb3JtYWNpw7NuLCBkZSBjb25mb3JtaWRhZCBjb24gbGEgbm9ybWF0aXZpZGFkIHZpZ2VudGUgc29icmUgZGVyZWNob3MgZGUgYXV0b3IgeSBkZXJlY2hvcyBjb25leG9zIHJlZmVyaWRvcyBlbiBhcnQuIDIsIDEyLCAzMCAobW9kaWZpY2FkbyBwb3IgZWwgYXJ0IDUgZGUgbGEgbGV5IDE1MjAvMjAxMiksIHkgNzIgZGUgbGEgbGV5IDIzIGRlIGRlIDE5ODIsIExleSA0NCBkZSAxOTkzLCBhcnQuIDQgeSAxMSBEZWNpc2nDs24gQW5kaW5hIDM1MSBkZSAxOTkzIGFydC4gMTEsIERlY3JldG8gNDYwIGRlIDE5OTUsIENpcmN1bGFyIE5vIDA2LzIwMDIgZGUgbGEgRGlyZWNjacOzbiBOYWNpb25hbCBkZSBEZXJlY2hvcyBkZSBhdXRvciwgYXJ0LiAxNSBMZXkgMTUyMCBkZSAyMDEyLCBsYSBMZXkgMTkxNSBkZSAyMDE4IHkgZGVtw6FzIG5vcm1hcyBzb2JyZSBsYSBtYXRlcmlhLgoKQWwgcmVzcGVjdG8gY29tbyBBdXRvcihlcykgbWFuaWZlc3RhbW9zIGNvbm9jZXIgcXVlOgoKLSBMYSBhdXRvcml6YWNpw7NuIGVzIGRlIGNhcsOhY3RlciBubyBleGNsdXNpdmEgeSBsaW1pdGFkYSwgZXN0byBpbXBsaWNhIHF1ZSBsYSBsaWNlbmNpYSB0aWVuZSB1bmEgdmlnZW5jaWEsIHF1ZSBubyBlcyBwZXJwZXR1YSB5IHF1ZSBlbCBhdXRvciBwdWVkZSBwdWJsaWNhciBvIGRpZnVuZGlyIHN1IG9icmEgZW4gY3VhbHF1aWVyIG90cm8gbWVkaW8sIGFzw60gY29tbyBsbGV2YXIgYSBjYWJvIGN1YWxxdWllciB0aXBvIGRlIGFjY2nDs24gc29icmUgZWwgZG9jdW1lbnRvLgoKLSBMYSBhdXRvcml6YWNpw7NuIHRlbmRyw6EgdW5hIHZpZ2VuY2lhIGRlIGNpbmNvIGHDsW9zIGEgcGFydGlyIGRlbCBtb21lbnRvIGRlIGxhIGluY2x1c2nDs24gZGUgbGEgb2JyYSBlbiBlbCByZXBvc2l0b3JpbywgcHJvcnJvZ2FibGUgaW5kZWZpbmlkYW1lbnRlIHBvciBlbCB0aWVtcG8gZGUgZHVyYWNpw7NuIGRlIGxvcyBkZXJlY2hvcyBwYXRyaW1vbmlhbGVzIGRlbCBhdXRvciB5IHBvZHLDoSBkYXJzZSBwb3IgdGVybWluYWRhIHVuYSB2ZXogZWwgYXV0b3IgbG8gbWFuaWZpZXN0ZSBwb3IgZXNjcml0byBhIGxhIGluc3RpdHVjacOzbiwgY29uIGxhIHNhbHZlZGFkIGRlIHF1ZSBsYSBvYnJhIGVzIGRpZnVuZGlkYSBnbG9iYWxtZW50ZSB5IGNvc2VjaGFkYSBwb3IgZGlmZXJlbnRlcyBidXNjYWRvcmVzIHkvbyByZXBvc2l0b3Jpb3MgZW4gSW50ZXJuZXQgbG8gcXVlIG5vIGdhcmFudGl6YSBxdWUgbGEgb2JyYSBwdWVkYSBzZXIgcmV0aXJhZGEgZGUgbWFuZXJhIGlubWVkaWF0YSBkZSBvdHJvcyBzaXN0ZW1hcyBkZSBpbmZvcm1hY2nDs24gZW4gbG9zIHF1ZSBzZSBoYXlhIGluZGV4YWRvLCBkaWZlcmVudGVzIGFsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwgZGUgbGEgSW5zdGl0dWNpw7NuLCBkZSBtYW5lcmEgcXVlIGVsIGF1dG9yKHJlcykgdGVuZHLDoW4gcXVlIHNvbGljaXRhciBsYSByZXRpcmFkYSBkZSBzdSBvYnJhIGRpcmVjdGFtZW50ZSBhIG90cm9zIHNpc3RlbWFzIGRlIGluZm9ybWFjacOzbiBkaXN0aW50b3MgYWwgZGUgbGEgSW5zdGl0dWNpw7NuIHNpIGRlc2VhIHF1ZSBzdSBvYnJhIHNlYSByZXRpcmFkYSBkZSBpbm1lZGlhdG8uCgotIExhIGF1dG9yaXphY2nDs24gZGUgcHVibGljYWNpw7NuIGNvbXByZW5kZSBlbCBmb3JtYXRvIG9yaWdpbmFsIGRlIGxhIG9icmEgeSB0b2RvcyBsb3MgZGVtw6FzIHF1ZSBzZSByZXF1aWVyYSBwYXJhIHN1IHB1YmxpY2FjacOzbiBlbiBlbCByZXBvc2l0b3Jpby4gSWd1YWxtZW50ZSwgbGEgYXV0b3JpemFjacOzbiBwZXJtaXRlIGEgbGEgaW5zdGl0dWNpw7NuIGVsIGNhbWJpbyBkZSBzb3BvcnRlIGRlIGxhIG9icmEgY29uIGZpbmVzIGRlIHByZXNlcnZhY2nDs24gKGltcHJlc28sIGVsZWN0csOzbmljbywgZGlnaXRhbCwgSW50ZXJuZXQsIGludHJhbmV0LCBvIGN1YWxxdWllciBvdHJvIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2NlcikuCgotIExhIGF1dG9yaXphY2nDs24gZXMgZ3JhdHVpdGEgeSBzZSByZW51bmNpYSBhIHJlY2liaXIgY3VhbHF1aWVyIHJlbXVuZXJhY2nDs24gcG9yIGxvcyB1c29zIGRlIGxhIG9icmEsIGRlIGFjdWVyZG8gY29uIGxhIGxpY2VuY2lhIGVzdGFibGVjaWRhIGVuIGVzdGEgYXV0b3JpemFjacOzbi4KCi0gQWwgZmlybWFyIGVzdGEgYXV0b3JpemFjacOzbiwgc2UgbWFuaWZpZXN0YSBxdWUgbGEgb2JyYSBlcyBvcmlnaW5hbCB5IG5vIGV4aXN0ZSBlbiBlbGxhIG5pbmd1bmEgdmlvbGFjacOzbiBhIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcy4gRW4gY2FzbyBkZSBxdWUgZWwgdHJhYmFqbyBoYXlhIHNpZG8gZmluYW5jaWFkbyBwb3IgdGVyY2Vyb3MgZWwgbyBsb3MgYXV0b3JlcyBhc3VtZW4gbGEgcmVzcG9uc2FiaWxpZGFkIGRlbCBjdW1wbGltaWVudG8gZGUgbG9zIGFjdWVyZG9zIGVzdGFibGVjaWRvcyBzb2JyZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBsYSBvYnJhIGNvbiBkaWNobyB0ZXJjZXJvLgoKLSBGcmVudGUgYSBjdWFscXVpZXIgcmVjbGFtYWNpw7NuIHBvciB0ZXJjZXJvcywgZWwgbyBsb3MgYXV0b3JlcyBzZXLDoW4gcmVzcG9uc2FibGVzLCBlbiBuaW5nw7puIGNhc28gbGEgcmVzcG9uc2FiaWxpZGFkIHNlcsOhIGFzdW1pZGEgcG9yIGxhIGluc3RpdHVjacOzbi4KCi0gQ29uIGxhIGF1dG9yaXphY2nDs24sIGxhIGluc3RpdHVjacOzbiBwdWVkZSBkaWZ1bmRpciBsYSBvYnJhIGVuIMOtbmRpY2VzLCBidXNjYWRvcmVzIHkgb3Ryb3Mgc2lzdGVtYXMgZGUgaW5mb3JtYWNpw7NuIHF1ZSBmYXZvcmV6Y2FuIHN1IHZpc2liaWxpZGFkCgo=

Publicaciones similares