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Purpose

Glaucoma leads to pathological loss of axons in the retinal nerve fibre layer at the optic nerve head (ONH). This study aimed to develop a strategy for the estimation of the cross-sectional area of the axons in the ONH. Furthermore, improving the estimation of the thickness of the nerve fibre layer, as compared to a method previously published by us.

Methods

In the 3D-OCT image of the ONH, the central limit of the pigment epithelium and the inner limit of the retina, respectively, were identified with deep learning algorithms. The minimal distance was estimated at equidistant angles around the circumference of the ONH. The cross-sectional area was estimated by the computational algorithm. The computational algorithm was applied on 16 non-glaucomatous subjects.

Results

The mean cross-sectional area of the waist of the nerve fibre layer in the ONH was 1.97 ± 0.19 mm². The mean difference in minimal thickness of the waist of the nerve fibre layer between our previous and the current strategies was estimated as CIμ (0.95) 0 ± 1 μm (d.f. = 15).

Conclusions

The developed algorithm demonstrated an undulating cross-sectional area of the nerve fibre layer at the ONH. Compared to studies using radial scans, our algorithm resulted in slightly higher values for cross-sectional area, taking the undulations of the nerve fibre layer at the ONH into account. The new algorithm for estimation of the thickness of the waist of the nerve fibre layer in the ONH yielded estimates of the same order as our previous algorithm.

Glaucoma is a global disease that leads to blindness due to pathological loss of retinal ganglion cell axons in the optic nerve head (ONH). The presented project aims at improving a computational algorithm for estimating the thickness and surface area of the waist of the nerve fiber layer in the ONH. Our currently developed deep learning AI algorithm meets the need for a morphometric parameter that detects glaucomatous change earlier than current clinical follow-up methods. In 3D OCT image volumes, two different AI algorithms identify the Optic nerve head Pigment epithelium Central Limit (OPCL) and the Inner limit of the Retina Closest Point (IRCP) in a 3D grid. Our computational algorithm includes the undulating surface area of the waist of the ONH, as well as waist thickness. In 16 eyes of 16 non-glaucomatous subjects aged [20;30] years, the mean difference in minimal thickness of the waist of the nerve fiber layer between our previous and the current post-processing strategies was estimated as CI mu(0.95) 0 +/- 1 mu m (D.f. 15). The mean surface area of the waist of the nerve fiber layer in the optic nerve head was 1.97 +/- 0.19 mm(2). Our computational algorithm results in slightly higher values for surface areas compared to published work, but as expected, this may be due to surface undulations of the waist being considered. Estimates of the thickness of the waist of the ONH yields estimates of the same order as our previous computational algorithm.

The present project aims at developing a fully automatic software for estimation of the waist of the nerve fiber layer in the Optic Nerve Head (ONH) angularly resolved in the frontal plane as a tool for morphometric monitoring of glaucoma. The waist of the nerve fiber layer is here defined as Pigment epithelium central limit –Inner limit of the retina – Minimal Distance, (PIMD). 3D representations of the ONH were collected with high resolution OCT in young not glaucomatous eyes and glaucomatous eyes. An improved tool for manual annotation was developed in Python. This tool was found user friendly and to provide sufficiently precise manual annotation. PIMD was automatically estimated with a software consisting of one AI model for detection of the inner limit of the retina and another AI model for localization of the Optic nerve head Pigment epithelium Central limit (OPCL). In the current project, the AI model for OPCL localization was retrained with new data manually annotated with the improved tool for manual annotation both in not glaucomatous eyes and in glaucomatous eyes. Finally, automatic annotations were compared to 3 annotations made by 3 independent annotators in an independent subset of both the not glaucomatous and the glaucomatous eyes. It was found that the fully automatic estimation of PIMD-angle overlapped the 3 manual annotators with small variation among the manual annotators. Considering interobserver variation, the improved tool for manual annotation provided less variation than our original annotation tool in not glaucomatous eyes suggesting that variation in glaucomatous eyes is due to variable pathological anatomy, difficult to annotate without error. The small relative variation in relation to the substantial overall loss of PIMD in the glaucomatous eyes compared to the not glaucomatous eyes suggests that our software for fully automatic estimation of PIMD-angle can now be implemented clinically for monitoring of glaucoma progression.