Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis
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ORIGINAL PAPER
Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis Hina Raja 1 & M. Usman Akram 1 & Arslan Shaukat 1 & Shoab Ahmed Khan 1 & Norah Alghamdi 2 & Sajid Gul Khawaja 1 & Noman Nazir 3 Received: 19 March 2020 / Revised: 16 June 2020 / Accepted: 9 September 2020 # Society for Imaging Informatics in Medicine 2020
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. Keywords Convolution neural network (CNN) . Inner limiting membrane (ILM) . Retinal pigmented epithelium (RPE) . Cup-to-disc ratio (CDR) . Glaucoma
Introduction Glaucoma is a progressive and degenerative optic neuropathy that causes visual field defects, which further leads to permanent blindness [1]. Glaucoma is an eye condition that occurs
due to increase in intraocular pressure (IOP). In early stages of glaucoma, peripheral vision loss occurs, which is difficult for even patient to diagnose. It has been witnessed that significant retinal nerve fiber layer (RNFL) loss occurs approximately 6 years prior to any detectable visual field defects [2, 3]. As
* Hina Raja [email protected]
Sajid Gul Khawaja [email protected] Noman Nazir [email protected]
M. Usman Akram [email protected] Arslan Shaukat [email protected]
1
Department of Computer and Sof
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