Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105553
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSantos, Valentin Aranha dosen
dc.contributor.authorSchmetterer, Leopolden
dc.contributor.authorStegmann, Hannesen
dc.contributor.authorPfister, Martinen
dc.contributor.authorMessner, Alinaen
dc.contributor.authorSchmidinger, Geralden
dc.contributor.authorGarhofer, Gerharden
dc.contributor.authorWerkmeister, René M.en
dc.date.accessioned2019-03-14T07:03:12Zen
dc.date.accessioned2019-12-06T21:53:30Z-
dc.date.available2019-03-14T07:03:12Zen
dc.date.available2019-12-06T21:53:30Z-
dc.date.issued2019en
dc.identifier.citationSantos, V. A. d., Schmetterer, L., Stegmann, H., Pfister, M., Messner, A., Schmidinger, G., . . . Werkmeister, R. M. (2019). CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomedical Optics Express, 10(2), 622-641. doi:10.1364/BOE.10.000622en
dc.identifier.urihttps://hdl.handle.net/10356/105553-
dc.description.abstractDeep 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.format.extent20 p.en
dc.language.isoenen
dc.relation.ispartofseriesBiomedical Optics Expressen
dc.rights© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.en
dc.subjectDeep Learning Algorithmsen
dc.subjectDRNTU::Science::Medicineen
dc.subjectOptical Coherence Tomographyen
dc.titleCorneaNet : fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learningen
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en
dc.identifier.doi10.1364/BOE.10.000622en
dc.description.versionPublished versionen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:LKCMedicine Journal Articles

SCOPUSTM   
Citations 5

98
Updated on Mar 22, 2024

Web of ScienceTM
Citations 5

74
Updated on Oct 30, 2023

Page view(s) 50

449
Updated on Mar 27, 2024

Download(s) 50

132
Updated on Mar 27, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.