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Automated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopy

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dc.contributor.advisorStanciu, Stefan G.
dc.contributor.advisorBueno, Juan M.
dc.contributor.authorAnton, Stefan
dc.contributor.authorMartínez Ojeda, Rosa M.
dc.contributor.authorHristu, Radu
dc.contributor.authorStanciu, George A.
dc.contributor.authorToma, Antonela
dc.contributor.authorBanica, Cosmin K.
dc.contributor.authorFernández, Enrique J.
dc.contributor.authorHuttunen, Mikko J.
dc.contributor.authorBueno, Juan M.
dc.contributor.authorStanciu, Stefan G.
dc.contributor.departmentFísica
dc.date.accessioned2024-02-06T09:12:55Z
dc.date.available2024-02-06T09:12:55Z
dc.date.issued2023-11-06
dc.description©2023. This document is the Published, version of a Published Work that appeared in final form in IEEE Journal of Selected Topics in Quantum Electronics. To access the final edited and published work see https://doi.org/10.1109/JSTQE.2023.3258687
dc.description.abstractSecond Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues.However, the interpretation ofSHGdata can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification ofcornea SHG images and demonstrate an AU-ROC=0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.es
dc.formatapplication/pdfes
dc.format.extent10es
dc.identifier.citationIEEE Journal of Selected Topics in Quantum Electronic, vol. 29, Nº 6, November/december 2023
dc.identifier.issn1077-260X
dc.identifier.issn1558-4542 (eISSN)
dc.identifier.urihttp://hdl.handle.net/10201/138724
dc.languageenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relationUEFISCDI under Grant PN-III-P2-2.1-PED-2019- 1666. Academy of Finland under Grant 320165. Agencia Estatal de Investigación under Grant PID2020-113919RBI00/AEI/10.13039/501100011033.es
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectCorneal edemaes
dc.subjectdeep learninges
dc.subjectOptical imaginges
dc.subjectOptical harmonic generationes
dc.subjectOptical microscopyes
dc.subject.otherCDU::5 - Ciencias puras y naturales::53 - Física::535 - Ópticaes
dc.titleAutomated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopyes
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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