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Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

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A Correction to this article was published on 25 June 2021

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Abstract

Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.

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Acknowledgments

The data was provided by the Armed Forces Institute of Ophthalmology (AFIO), Pakistan, and the research was carried out under Biomedical Image/Signal Analysis (BIOMISA) Research Lab in NUST, Islamabad, Pakistan.

Funding

Higher Education Commission (HEC) Pakistan funded this researcher. This research was co-funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.

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Authors and Affiliations

  1. Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan

    Hina Raja, M. Usman Akram, Arslan Shaukat, Shoab Ahmed Khan & Sajid Gul Khawaja

  2. Department of Computer Science, Princess Nora Bint Abdurahman University, Riyadh, Saudi Arabia

    Norah Alghamdi

  3. Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan

    Noman Nazir

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  1. Hina Raja
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Correspondence to Hina Raja.

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Raja, H., Akram, M.U., Shaukat, A. et al. Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis. J Digit Imaging 33, 1428–1442 (2020). https://doi.org/10.1007/s10278-020-00383-5

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