Dataset Information

OCT-Detected Optic Nerve Head Neural Canal Direction, Obliqueness, and Minimum Cross-Sectional Area in Healthy Eyes.

ABSTRACT: PURPOSE:To assess anterior scleral canal opening (ASCO) offset relative to Bruch's membrane opening (BMO) (ASCO/BMO offset) so as to determine neural canal direction, obliqueness, and minimum cross-sectional area (NCMCA) in 362 healthy eyes. DESIGN:Cross-sectional study. METHODS:After optical coherence tomography optic nerve head and retinal nerve fiber layer thickness (RNFLT) imaging, BMO and ASCO were manually segmented. Planes, centroids, size, and shape were calculated. Neural canal direction was defined by projecting the neural canal axis vector (connecting BMO and ASCO centroids) onto the BMO plane. Neural canal obliqueness was defined by the angle between the neural canal axis and the BMO plane perpendicular vector. NCMCA was defined by projecting BMO and ASCO points onto a neural canal axis perpendicular plane and measuring the area of overlap. The angular distance between superior and inferior peak RNFLT was measured, and correlations between RFNLT, BMO, ASCO, ASCO/BMO offset, and NCMCA were assessed. RESULTS:Mean (SD) NCMCA was significantly smaller than either the BMO or ASCO area (1.33 (0.42), 1.82 (0.38), 2.22 (0.43) mm², respectively), and most closely correlated to RNFLT (P < .001, R² = 0.158). Neural canal direction was most commonly superior-nasal (55%). Mean neural canal obliqueness was 39.4° (17.3°). The angular distance between superior and inferior peak RNFLT correlated to neural canal direction (P ? .008, R² = 0.093). CONCLUSIONS:ASCO/BMO offset underlies neural canal direction, obliqueness, and NCMCA. RNFLT is more strongly correlated to NCMCA than to BMO or ASCO, and its peripapillary distribution is influenced by neural canal direction.

SUBMITTER: Hong S

PROVIDER: S-EPMC6851461 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

OCT-Detected Optic Nerve Head Neural Canal Direction, Obliqueness, and Minimum Cross-Sectional Area in Healthy Eyes.

Hong Seungwoo S Yang Hongli H Gardiner Stuart K SK Luo Haomin H Hardin Christy C Sharpe Glen P GP Caprioli Joseph J Demirel Shaban S Girkin Christopher A CA Liebmann Jeffrey M JM Mardin Christian Y CY Quigley Harry A HA Scheuerle Alexander F AF Fortune Brad B Chauhan Balwantray C BC Burgoyne Claude F CF

American journal of ophthalmology 20190513

<h4>Purpose</h4>To assess anterior scleral canal opening (ASCO) offset relative to Bruch's membrane opening (BMO) (ASCO/BMO offset) so as to determine neural canal direction, obliqueness, and minimum cross-sectional area (NCMCA) in 362 healthy eyes.<h4>Design</h4>Cross-sectional study.<h4>Methods</h4>After optical coherence tomography optic nerve head and retinal nerve fiber layer thickness (RNFLT) imaging, BMO and ASCO were manually segmented. Planes, centroids, size, and shape were calculated. ...[more]

PMID: 31095953

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Project description:ObjectiveTo develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH).DesignEvaluation of a diagnostic test or technology.ParticipantsHealthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL).MethodsWe present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high-frequency OCT imaging and widely available image processing algorithms. We performed a thorough validation of the approach, numerically and experimentally, evaluating the sensitivity of the method to artificially induced deformation and its robustness to different noise levels. We performed deformation measurements in cohorts of healthy (n = 9) and myopic (n = 5) subjects in different physiological strain conditions by calculating the amplitude of tissue displacement in both the primary position and abduction. The head rotation was measured using a goniometer. During imaging in abduction, the head was rotated 40° ± 3°, and subjects were instructed to direct their gaze toward the OCT visual target.Main outcome measuresPulsatile tissue displacement maps.ResultsThe robustness of the method was assessed using artificial deformations and increasing noise levels. The results show acceptable absolute errors before the noise simulations grossly exaggerate image degradation. For the group of subjects with AL of < 25 mm (n = 9), the median pulsatile displacement of the ONH was 7.8 ± 1.3 μm in the primary position and 8.9 ± 1.2 μm in abduction. The Wilcoxon test showed a significant difference (P ≤ 0.005) between the 2 paired measures. Reproducibility was tested in 2 different sessions in 5 different subjects with the same intraocular pressure, and an intraclass correlation coefficient of 0.99 was obtained (P < 0.005).ConclusionsThe computational pipeline demonstrated good reproducibility and had the capacity to accurately map the pulsatile deformation of the optic nerve. In a clinical setting, we detected physiological changes in normal subjects supporting its translation potential as a novel biomarker for the diagnosis and progression of optic nerve diseases.

Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Dataset Information

OCT-Detected Optic Nerve Head Neural Canal Direction, Obliqueness, and Minimum Cross-Sectional Area in Healthy Eyes.

Publications

OCT-Detected Optic Nerve Head Neural Canal Direction, Obliqueness, and Minimum Cross-Sectional Area in Healthy Eyes.

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