Sripad Krishna Devalla1, Khai Sing Chin2, Jean-Martial Mari3, Tin A Tun4, Nicholas G Strouthidis4,5,6, Tin Aung4,7, Alexandre H Thiéry2, Michaël J A Girard1,4. 1. Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore. 2. Department of Statistics and Applied Probability, National University of Singapore, Singapore. 3. GePaSud, Université de la Polynésie Française, Tahiti, French Polynesia. 4. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. 5. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom. 6. Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Sydney, New South Wales, Australia. 7. Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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 < 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.
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 < 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.
Authors: Sripad Krishna Devalla; Prajwal K Renukanand; Bharathwaj K Sreedhar; Giridhar Subramanian; Liang Zhang; Shamira Perera; Jean-Martial Mari; Khai Sing Chin; Tin A Tun; Nicholas G Strouthidis; Tin Aung; Alexandre H Thiéry; Michaël J A Girard Journal: Biomed Opt Express Date: 2018-06-25 Impact factor: 3.732
Authors: Mark Christopher; Akram Belghith; Christopher Bowd; James A Proudfoot; Michael H Goldbaum; Robert N Weinreb; Christopher A Girkin; Jeffrey M Liebmann; Linda M Zangwill Journal: Sci Rep Date: 2018-11-12 Impact factor: 4.379
Authors: Mark Christopher; Christopher Bowd; Akram Belghith; Michael H Goldbaum; Robert N Weinreb; Massimo A Fazio; Christopher A Girkin; Jeffrey M Liebmann; Linda M Zangwill Journal: Ophthalmology Date: 2019-09-30 Impact factor: 12.079