Faculty Bibliography

The broadening frontier of technology used in ocular imaging is continuously affecting the landscape of clinical eye care. With each wave of enhanced imaging modalities, the field faces the difficulties of optimally incorporating these devices into the clinic. Ocular imaging devices have been widely incorporated into clinical management after their diagnostic capabilities have been documented in a wide range of ocular disease. In this review, we are presenting the main commercially available devices for imaging of the posterior segment of the eye.

PURPOSE: Commercial optical coherence tomography (OCT) systems use global signal quality indices to quantify scan quality. Signal quality can vary throughout a scan, contributing to local retinal nerve fibre layer segmentation errors (SegE). The purpose of this study was to develop an automated method, using local scan quality, to predict SegE. METHODS: Good-quality (global signal strength (SS) >/= 6; manufacturer specification) peripapillary circular OCT scans (fast retinal nerve fibre layer scan protocol; Stratus OCT; Carl Zeiss Meditec, Dublin, California, USA) were obtained from 6 healthy, 19 glaucoma-suspect and 43 glaucoma subjects. Scans were grouped based on SegE. Quality index (QI) values were computed for each A-scan using software of our own design. Logistic mixed-effects regression modelling was applied to evaluate SS, global mean and SD of QI, and the probability of SegE. RESULTS: The difference between local mean QI in SegE regions and No-SegE regions was -5.06 (95% CI -6.38 to 3.734) (p<0.001). Using global mean QI, QI SD and their interaction term resulted in the model of best fit (Akaike information criterion=191.8) for predicting SegE. Global mean QI >/= 20 or SS >/= 8 shows little chance for SegE. Once mean QI<20 or SS<8, the probability of SegE increases as QI SD increases. CONCLUSIONS: When combined with a signal quality parameter, the variation of signal quality between A-scans provides significant information about the quality of an OCT scan and can be used as a predictor of segmentation error.

AIMS: To determine the retinal nerve fibre layer (RNFL) thickness at which visual field (VF) damage becomes detectable and associated with structural loss. METHODS: In a prospective cross-sectional study, 72 healthy and 40 glaucoma subjects (one eye per subject) recruited from an academic institution had VF examinations and spectral domain optical coherence tomography (SD-OCT) optic disc cube scans (Humphrey field analyser and Cirrus HD-OCT, respectively). Comparison of global mean and sectoral RNFL thicknesses with VF threshold values showed a plateau of threshold values at high RNFL thicknesses and a sharp decrease at lower RNFL thicknesses. A 'broken stick' statistical model was fitted to global and sectoral data to estimate the RNFL thickness 'tipping point' where the VF threshold values become associated with the structural measurements. The slope for the association between structure and function was computed for data above and below the tipping point. RESULTS: The mean RNFL thickness threshold for VF loss was 75.3 mum (95% CI: 68.9 to 81.8), reflecting a 17.3% RNFL thickness loss from age-matched normative value. Above the tipping point, the slope for RNFL thickness and threshold value was 0.03 dB/mum (CI: -0.02 to 0.08) and below the tipping point, it was 0.28 dB/mum (CI: 0.18 to 0.38); the difference between the slopes was statistically significant (p<0.001). A similar pattern was observed for quadrant and clock-hour analysis. CONCLUSIONS: Substantial structural loss ( approximately 17%) appears to be necessary for functional loss to be detectable using the current testing methods.

We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.

PURPOSE: To develop an automated method to identify the normal macula and three macular pathologies (macular hole [MH], macular edema [ME], and age-related macular degeneration [AMD]) from the fovea-centered cross sections in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images. METHODS: A sample of SD-OCT macular scans (macular cube 200 x 200 or 512 x 128 scan protocol; Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) was obtained from healthy subjects and subjects with MH, ME, and/or AMD (dataset for development: 326 scans from 136 subjects [193 eyes], and dataset for testing: 131 scans from 37 subjects [58 eyes]). A fovea-centered cross-sectional slice for each of the SD-OCT images was encoded using spatially distributed multiscale texture and shape features. Three ophthalmologists labeled each fovea-centered slice independently, and the majority opinion for each pathology was used as the ground truth. Machine learning algorithms were used to identify the discriminative features automatically. Two-class support vector machine classifiers were trained to identify the presence of normal macula and each of the three pathologies separately. The area under the receiver operating characteristic curve (AUC) was calculated to assess the performance. RESULTS: The cross-validation AUC result on the development dataset was 0.976, 0.931, 0939, and 0.938, and the AUC result on the holdout testing set was 0.978, 0.969, 0.941, and 0.975, for identifying normal macula, MH, ME, and AMD, respectively. CONCLUSIONS: The proposed automated data-driven method successfully identified various macular pathologies (all AUC > 0.94). This method may effectively identify the discriminative features without relying on a potentially error-prone segmentation module.

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class support vector machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC>0.93).

Aqueous humor (AH) exiting the eye via the trabecular meshwork and Schlemm's canal (SC) passes through the deep and intrascleral venous plexus (ISVP) or directly through aqueous veins. The purpose of this study was to visualize the human AH outflow system 360 degrees in three dimensions (3D) during active AH outflow in a virtual casting. The conventional AH outflow pathways of 7 donor eyes were imaged with a modified Bioptigen spectral-domain optical coherence tomography system (Bioptigen Inc, USA; SuperLum LTD, Ireland) at a perfusion pressure of 20 mmHg (N = 3), and 10 mmHg (N = 4). In all eyes, 36 scans (3 equally distributed in each clock hour), each covering a 2 x 3 x 2 mm volume (512 frames, each 512 x 1024 pixels), were obtained. All image data were black/white inverted, and the background subtracted (ImageJ 1.40 g, http://rsb.info.nih.gov/ij/). Contrast was adjusted to isolate the ISVP. SC, collector channels, the deep and ISVP, and episcleral veins were observed throughout the limbus. Aqueous veins could be observed extending into the episcleral veins. Individual scan ISVP castings were rendered and assembled in 3D space in Amira 4.1 (Visage Imaging Inc. USA). A 360-degree casting of the ISVP was obtained in all perfused eyes. The ISVP tended to be dense and overlapping in the superior and inferior quadrants, and thinner in the lateral quadrants. The human AH outflow pathway can be imaged using SD-OCT. The more superficial structures of the AH outflow pathway present with sufficient contrast as to be optically isolated and cast in-situ 360 degrees in cadaver eye perfusion models. This approach may be useful as a model in future studies of human AH outflow.

Detection of disease progression is an important and challenging component of glaucoma management. Optical coherence tomography (OCT) has proved to be valuable in the detection of glaucomatous damage. With its high resolution and proven measurement reproducibility, OCT has the potential to become an important tool for glaucoma progression detection. This manuscript presents the capabilities of the OCT technology pertinent for detection of progressive glaucomatous damage and provides a review of the current knowledge on the device's clinical performance.

PURPOSE: To investigate the longitudinal effect of optic nerve crush injury in mice by measuring retinal thickness with spectral-domain optical coherence tomography (SD-OCT). METHODS: Optic nerves of one eye from each C57Bl/6 mouse were crushed under direct visualization for 3 seconds, 1 mm posterior to the globe. The optic nerve head (ONH) was imaged with SD-OCT (1.5 x 1.5 x 2.0 mm scan) before the surgical intervention and repeated subsequently for up to 32 days postinjury. A cohort of mice not exposed to the nerve crush procedure served as control. En face SD-OCT images were used to manually align subsequent scans to the baseline en face image. Total retinal thickness (TRT) (along a sampling band with radii 0.33-0.42 mm centered on the ONH) from each follow-up day was automatically quantified for global and sectoral measurements using custom software. Linear mixed-effects models with quadratic terms were fitted to compare TRT of nerve-crushed and control eyes over time. RESULTS: Eleven eyes from 11 nerve crush mice (baseline age 76 +/- 11.8 days) and eight eyes from four healthy mice (baseline age 64 +/- 0 days) were included. The control eyes showed a small, gradual, and consistent TRT increase throughout follow-up. Nerve-crushed eyes showed an initial period of thickening, followed by thinning and slight rebound after day 21. The decrease in thickness observed after the early thickening resolved was statistically significantly different from the control eyes (P < 0.05 for global and sectoral measurements). CONCLUSIONS: SD-OCT can be used to quantitatively monitor changes in retinal thickness in mice over time.