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

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Automated_Detection_of_Corneal_Edema_With_Deep_Learning-Assisted_Second_Harmonic_Generation_Microscopy.pdf(1.9 MB)
Date
2023-11-06
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Authors
Anton, Stefan ; Martínez Ojeda, Rosa M. ; Hristu, Radu ; Stanciu, George A. ; Toma, Antonela ; Banica, Cosmin K. ; Fernández, Enrique J. ; Huttunen, Mikko J. ; Bueno, Juan M. ; Stanciu, Stefan G.
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Stanciu, Stefan G. ; Bueno, Juan M.
Publisher
Institute of Electrical and Electronics Engineers
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Física
DOI
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info:eu-repo/semantics/article
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
Abstract
Second 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.
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Corneal edema , deep learning , Optical imaging , Optical harmonic generation , Optical microscopy
Citation
IEEE Journal of Selected Topics in Quantum Electronic, vol. 29, Nº 6, November/december 2023
URI
http://hdl.handle.net/10201/138724
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