Please use this identifier to cite or link to this item: https://doi.org/10.1167/iovs.17-22617
Title: A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head
Authors: Devalla, S.K.
Chin, K.S.
Mari, J.-M.
Tun, T.A.
Strouthidis, N.
Aung, T. 
Thiéry, A.H. 
Girard, M.J.A. 
Keywords: Adaptive compensation
Artificial intelligence
Deep learning
Digital staining
Glaucoma
Optic nerve head
Optical coherence tomography
Issue Date: 2018
Publisher: Association for Research in Vision and Ophthalmology Inc.
Citation: Devalla, S.K., Chin, K.S., Mari, J.-M., Tun, T.A., Strouthidis, N., Aung, T., Thiéry, A.H., Girard, M.J.A. (2018). A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head. Investigative Ophthalmology and Visual Science 59 (1) : 63-74. ScholarBank@NUS Repository. https://doi.org/10.1167/iovs.17-22617
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: PURPOSE. To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). METHODS. A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. RESULTS. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer + prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 ± 0.03, 0.92 ± 0.03, 0.99 ± 0.00, 0.89 ± 0.03, and 0.94 ± 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P & LT; 0.001). Besides offering a good reliability, digital staining also performed well on OCT images of both glaucoma and healthy individuals. CONCLUSIONS. Our deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management. © 2018 The Authors.
Source Title: Investigative Ophthalmology and Visual Science
URI: https://scholarbank.nus.edu.sg/handle/10635/210134
ISSN: 0146-0404
DOI: 10.1167/iovs.17-22617
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1167_iovs_17-22617.pdf1.36 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

55
checked on Sep 30, 2022

Page view(s)

68
checked on Oct 6, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons