Objective: Glaucoma is the second leading cause of blindness worldwide.
Glaucomatous progression can be easily monitored by analyzing the degeneration
of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by
measuring cup-to-disc ratios from fundus and optical coherence tomography
scans. However, this paper presents a novel strategy that pays attention to the
RGC atrophy for screening glaucomatous pathologies and grading their severity.
Methods: The proposed framework encompasses a hybrid convolutional network that
extracts the retinal nerve fiber layer, ganglion cell with the inner plexiform
layer and ganglion cell complex regions, allowing thus a quantitative screening
of glaucomatous subjects. Furthermore, the severity of glaucoma in screened
cases is objectively graded by analyzing the thickness of these regions.
Results: The proposed framework is rigorously tested on publicly available
Armed Forces Institute of Ophthalmology (AFIO) dataset, where it achieved the
F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of
0.8697 for extracting the RGC regions and an accuracy of 0.9117 for grading
glaucomatous progression. Furthermore, the performance of the proposed
framework is clinically verified with the markings of four expert
ophthalmologists, achieving a statistically significant Pearson correlation
coefficient of 0.9236. Conclusion: An automated assessment of RGC degeneration
yields better glaucomatous screening and grading as compared to the
state-of-the-art solutions. Significance: An RGC-aware system not only screens
glaucoma but can also grade its severity and here we present an end-to-end
solution that is thoroughly evaluated on a standardized dataset and is
clinically validated for analyzing glaucomatous pathologies.
Hina Raja, Taimur Hassan, Muhammad Usman Akram, Naoufel Werghi