<div class="csl-bib-body">
<div class="csl-entry">dos Santos, V. A., Schmetterer, L., Stegmann, H., Pfister, M., Messner, A., Schmidinger, G., Garhofer, G., & Werkmeister, R. M. (2019). CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. <i>Biomedical Optics Express</i>, <i>10</i>(2), 622–641. https://doi.org/10.1364/boe.10.000622</div>
</div>
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dc.identifier.issn
2156-7085
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/144048
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dc.description.abstract
Deep learning has dramatically improved object recognition, speech recognition, medical image analysis and many other fields. Optical coherence tomography (OCT) has become a standard of care imaging modality for ophthalmology. We asked whether deep learning could be used to segment cornea OCT images. Using a custom-built ultrahigh-resolution OCT system, we scanned 72 healthy eyes and 70 keratoconic eyes. In total, 20,160 images were labeled and used for the training in a supervised learning approach. A custom neural network architecture called CorneaNet was designed and trained. Our results show that CorneaNet is able to segment both healthy and keratoconus images with high accuracy (validation accuracy: 99.56%). Thickness maps of the three main corneal layers (epithelium, Bowman’s layer and stroma) were generated both in healthy subjects and subjects suffering from keratoconus. CorneaNet is more than 50 times faster than our previous algorithm. Our results show that deep learning algorithm scan be used for OCT image segmentation and could be applied in various clinical settings. In particular, CorneaNet could be used for early detection of keratoconus and more generally to study other diseases altering corneal morphology.
en
dc.language.iso
en
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dc.relation.ispartof
Biomedical Optics Express
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dc.subject
Atomic and Molecular Physics
en
dc.subject
Biotechnology
en
dc.title
CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning
en
dc.type
Artikel
de
dc.type
Article
en
dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.contributor.affiliation
Nanyang Technological University, Singapore
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dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.contributor.affiliation
Medical University of Vienna, Austria
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dc.description.startpage
622
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dc.description.endpage
641
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dc.type.category
Original Research Article
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tuw.container.volume
10
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tuw.container.issue
2
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.researchTopic.id
M2
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tuw.researchTopic.name
Materials Characterization
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tuw.researchTopic.value
100
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dcterms.isPartOf.title
Biomedical Optics Express
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tuw.publication.orgunit
E134-01 - Forschungsbereich Applied and Computational Physics