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- Kisonaite, Konstancija, et al.
(author)
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Automatic estimation of the cross-sectional area of the waist of the nerve fiber layer at the optic nerve head
- 2023
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In: Acta Ophthalmologica. - : John Wiley & Sons. - 1755-375X .- 1755-3768.
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Journal article (peer-reviewed)abstract
- PurposeGlaucoma 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.MethodsIn 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.ResultsThe 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).ConclusionsThe 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.
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| 2. |
- Kisonaite, Konstancija
(author)
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Quantitative assessment of glaucoma by artificial intelligence estimation of the waist of the nerve fiber layer in the optic nerve head
- 2023
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Licentiate thesis (other academic/artistic)abstract
- Glaucoma is a chronic progressive disease that causes loss of retinal ganglion cells, which impairs the visual field. In optic coherence tomography (OCT) image, the retinal ganglion cell (RGC) axons in the optic nerve head (ONH) can be quantified as the minimal thickness from the ONH Pigmental epithelium Central Limit (OPCL) to the Inner limit of the Retina Closest Point (IRCP). Alternatively, the minimal cross-sectional surface area can be measured. In peripapillary atrophy, the morphometry of the retinal pigmental epithelium is affected.Purpose: To design and test a new computational algorithm for estimation of Pigment epithelium to Inner limit of the Retina Minimal Area (PIMA) and evaluate a new method to estimate the Pigment epithelium to Inner limit of the Retina Minimal Distance (PIMD). OPCL can be detected and annotated by a deep learning algorithm in individuals with peripapillary atrophy.Methods: A deep learning algorithm has been trained to automatically detect OPCL, IRCP and calculate PIMD. A new computational algorithm was developed to estimate PIMA in OCT images of young adults. The mean between the first and second version of estimating PIMD was evaluated. The difference of distance between the ONH center-OPCL and ONH center-atrophic edge was estimated in eyes with peripapillary atrophy.Results: A 95% confidence interval for PIMA-2π was estimated to 1.97 ± 0.19 mm2 (df = 15). A confidence interval for the difference between PIMDv1-2π and PIMDv2-2π was 0 ± 1 μm (df = 15). A 95 % confidence interval for the mean difference between ONH-OPCL and ONH-atrophic edge was estimated to 692 ± 192 µm (df = 5).Conclusions: The computational algorithm for estimation of PIMA was developed and applied. An initial analysis indicated the capacity of the deep learning algorithm to detect OPCL in subjects with PPA.Keywords: deep learning, optic nerve head, ONH, retinal pigmental epithelium, RPE, PIMD, PIMD-2π, minimal distance, PIMA, PIMA-2π, minimal area, peripapillary atrophy, PPA, optic coherence tomography, OCT, glaucoma, quantification, retinal ganglion cell axons
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