Faculty Bibliography

Purpose : The lamina cribrosa (LC) is considered to play an important role in the pathogenesis of glaucoma. In this study, we investigate the shape variation (SV) of the LC pores as a potential biomarker for quantifying the morphological irregularity in vivo. Methods : 36 healthy and 14 glaucomatous (GL) eyes (total: 39 subjects) underwent a comprehensive ophthalmic examination and scanning of the optic nerve head with sweptsource OCT (Table 1). Images were converted to isotropic and pores were segmented using ImageJ. SV was defined as the mean-squared error of the pore pattern with respect to a solid circle (Figure 1(a)) with SV of a circle marked as zero, and higher SV value with increasing shape irregularity. SV of each pore was automatically calculated by a MATLAB code. The overall SV value was generated as the average of SV in the stack of individual slices. Age effect on SV was examined in all healthy eyes and a subset of 14 eyes was selected for age-matched comparison with the glaucomatous eyes (Table 1). Results : No significant correlation was detected between SV and age (p=0.145; Spearman correlation) in all healthy subjects. Examining the effect of depth on the difference between SV of glaucomatous and healthy eyes, the posterior third of the LC had significantly lower than other sections (p=0.007; Figure 1(b)). SV in glaucoma eyes was significantly higher than in the healthy group (p=0.008; Figure 1(c)). Conclusions : We demonstrated morphological differences in pore shape variation between healthy and glaucoma eyes that is mostly affecting the anterior 2/3 of the LC. Further studies are warranted to assess the use of SV as a structural biomarker in glaucoma

Purpose : To investigate the least variable sampling location for OCT retinal nerve fiber layer (RNFL) thickness measurements on rhesus macaque monkeys, for determining the preferred sampling location. Methods : In vivo three-dimensional spectral-domain OCT scans (Leica, Chicago, IL) were obtained as raster scan data (400x400x1024) in a 5x5x1.6 mm region (human equivalent, not the actual size in the monkey eye) centered on the optic nerve head (ONH) of 33 healthy adult rhesus macaques (19 males, 14 females; ages 3.0-10.7 years). The ONH scans of 48 eyes were analyzed using OCT segmentation software of our own design to calculate point-by-point RNFL thickness measurements. Mean RNFL thickness was computed on consecutive concentric circles within the scan window, centered on the geometric ONH center and starting at the optic disc margin (between 64-119 circles). The least variable RNFL measurement area was identified in the vicinity of the RNFL peak within the 2 mum deviation. Results : The least variable RNFL was observed in between 98.88+/-11.82 and 114.4+/-11.32 pixels from the ONH center with the peak RNFL at 106.42+/-11.55 pixels (Figure 1). Note that the number of available eyes in each sampling location varied as detailed in Figure 2. For comparison, the radius of the OCT scan circle conventionally used in humans is 1.7 mm, or 136 pixels, from the center of the ONH. Conclusions : In order to obtain less variable circumpapillary RNFL thickness measurements on rhesus macaque monkey eyes, it is recommended to use a sampling circle with a radius of approximately 106 pixels from the ONH center, which is smaller than the human equivalent

Purpose : Intraocular pressure (IOP) is currently the only modifiable risk factor for glaucoma, yet glaucoma can continue to progress despite controlled IOP. Thus, development of glaucoma neurotherapeutics remains an unmet need. Scutellarin is a flavonoid that exhibits a number of neuroprotective effects on the brain and the eye. Here, we investigated the neurobehavioral effects of oral scutellarin treatment in a novel experimental model of chronic glaucoma. Methods : Ten adult C57BL/6J mice (Group 1) were unilaterally injected with an optically clear hydrogel into the anterior chamber to obstruct aqueous outflow and induce chronic IOP elevation. Eight other mice (Group 2) received a unilateral intracameral injection of phosphate-buffered saline only. Another eight mice (Group 3) with hydrogel-induced unilateral chronic IOP elevation also received daily oral gavage of 300 mg/kg scutellarin from 1 week before to 2 weeks after hydrogel injection. Tonometry, optical coherence tomography, and optokinetic visuobehavioral testing were performed longitudinally to monitor the IOP, total retinal thickness, visual acuity, and contrast threshold of bilateral eyes in all three groups. Results : Intracameral hydrogel injection resulted in unilateral chronic IOP elevation with no significant IOP difference between scutellarin treatment and untreated groups (Figure site uses cookies. By continuing to use our website, you are agreeing to 1). With scutellarin treatment, the hydrogel-injected eyes showed less retinal thinning and reduced visual behavioral deficits when compared to the untreated, hydrogel-injected eyes (Figure 2). No significant difference in retinal thickness or optokinetic measures was found in the non-injected eyes over time or between all groups. Conclusions : Oral scutellarin treatment appeared to preserve retinal structure and visual function in experimental glaucoma induced by chronic IOP elevation. Scutellarin may be a possible candidate as a novel neurotherapeutic agent for glaucoma treatment

Purpose : Current Optical Coherence Tomography (OCT) denoising techniques mainly focus on denoising 2-dimensional (2D) B-scans, especially for deep learning (DL) methods, which assume spatial integrity among neighboring samplings. However, OCT signal is essentially one dimensional (1D), and eye movements during scanning often violate the assumption. The purpose of this study was to see if 1D denoising is a feasible approach for clinical OCT imaging. Methods : 3D SD-OCT data within 6x6x2mm volumes centered on optic nerve head were obtained from 121 eyes (79 patients). Clean reference scans were constructed by registering and averaging 6 OCT scans obtained on the same day using ANTs software. As shown in Figure 1, we used a 5-layer U-shape net (UNet) for both 2D denoiser (Figure 1.(a)) and 1D denoiser (Figure1.(b)). In addition, both 2D and 1D approaches are combined by using the 2D denoised B-scan as a mask to selectively remove high signal peaks in the 1D denoised B-scan (Figure 1.(c)). Peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR) were calculated to compare the performance. Results : Subjectively, the 2D denoiser generated smoother edges but tended to oversmooth textual information within the retinal layers, while the 1D denoiser preserved more textual information but caused more jittering at retinal edges due to the lack of structural information from neighboring A-scans. Quantitatively, the 1D denoiser showed similar PSNR to the 2D denoiser, while outperforming in CNR (PSNR: 31.20 (1D) V.S. 31.20 dB (2D), p=0.963; CNR: 4.23 (1D) V.S. 3.90 dB (2D), p<0.001, paired t-test, Table 1). The combined denoiser further improved CNR (4.39 (combined) V.S. 3.90 dB (2D), p<0.001). Combining 1D and 2D denoisers, the denoised B-scan showed more continuous edges compared to the 1D denoiser and did not lose the texture information compared to the 2D denoiser (Figure 2). Conclusions : Quantitatively, 1D denoiser performance is as good as 2D denoiser or even better. A combination of 1D and 2D approaches may provide well-balanced image enhancement in clinical applications

Purpose : Repetitive transorbital alternating current stimulation (rtACS) is an application of weak electric current near the eyes used in vision rehabilitation of optic neuropathies (ON). Conceptually rtACS entrains neuronal oscillations, augmenting neuronal function. In subjects with ON we evaluated whether rtACS influenced visual structure and function. Methods : 34 subjects with ON enrolled in a prospective trial underwent comprehensive ophthalmic evaluation, visual field (VF) 24-2 and 10-2 tests (Humphrey Field Analyzer) and OCT (Cirrus HD-OCT) retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thicknesses at baseline and follow-up (FU) visits. Subjects received rtACS 30-to 45-minutes daily for 10 days. Sham subjects (n=4) underwent the same procedures but received no current. Point-by-point analyses of VF total deviation (TD) values were conducted between rtACS and sham groups. Regression analyses determined rate of change for each TD point per eye (significant points with positive rate of change defined as improved, negative rate of change as progressed; insignificant rate of change as no change) and the association between RNFL and GCIPL between groups. Results : The number of FU visits with VF tests ranged 2 to 7, with no significant differences detected between rtACS vs sham groups' FU duration. No significant differences were detected between groups' baseline VF 24-2 and 10-2 mean deviation (MD) values (Table 1). The average numbers of improved points (VF 10-2) and progressed points (VF 24-2) were greater for rtACS while the average number of no change points was greater for sham (VF 24-2, p0.05, Table 1). Further analysis of FU duration determined a significant interaction with rtACS; number of improved points (VF 10-2) and progressed points (VF 24-2, p<0.02) were not sustained over time. No significant differences were detected in average RNFL and GCIPL thicknesses between groups. Conclusions : Preliminary analyses of the effect of rtACS in ON indicate initial improvement but not a clear benefit over time. Detection of differences between rtACS vs sham groups may be biased due to the small sham sample and range of FU duration as VF test-to-test variability is known to increase with worsening VF MD. Future analyses will assess interim effect at early vs late FU time points to evaluate the role of rtACS in vision rehabilitation

Purpose : To improve the performance of the feature agnostic AI-based glaucoma detection algorithm by evaluating an uncertainty score for each prediction. Methods : We previously developed a 5-layer 3D Convolutional Neural Network (CNN) in using the OCT scans from both eyes of 134 healthy, 779 glaucoma patients on a Cirrus HDOCT scanner (200x200 ONH Cubes; Zeiss, Dublin CA). In our analysis, we excluded scans with signal strength less than 7 and downsampled the volumes to 64x64x128 voxels. Uncertainty of AI models can be estimated by computing the effect of randomly ignoring a set of parameters within the network. We randomly zeroed 5% of each of the 5 convolutional layers and computed the entropy in the final score over 20 forward passes. The performance of the approach was assessed using a 10-fold cross validation study. Results : Over the 10-folds, the model showed an AUC of 0.91+/-0.027. In analysing the uncertainty and the probabilistic scores generated by the model (Softmax function) for one fold (see Fig. 1), we observed that a threshold of 0.8 can be used to flag 75% of the false positives and false negatives for further review. On the other hand, only 25% of the healthy controls and 20% of glaucoma patients showed an uncertainty score above that threshold. Fig. 2 summarises the overall uncertainties scores and indicates that low scores are associated with the correctly identified cases while the errors show higher uncertainty scores. Conclusions : The quantitative uncertainty measure provides supplementary information to clinicians and can be used to flag difficult cases automatically. Given that the dataset used in this work is highly imbalanced (more positive cases compared to normal cases) the uncertainty score for true negative cases is significantly higher compared to true positive cases. We expect to achieve lower uncertainty scores for normal cases if more data for normal eyes are available. The uncertainty analysis presented here may aid clinical interpretations of AI-based glaucoma detection outcomes. A separate study will be run to measure this improvement and compare the result with experts' level of uncertainty

Purpose : The uncertainty quantification of segmentation results is critical for understanding the reliability of the segmentation model. The purpose of this study is to investigate the effect of deep learning-based segmentation with uncertainty measurement in the relationship between RNFL thickness and visual field mean deviation (MD) Methods : Optical coherence tomography (OCT) scans were acquired from both eyes on 634 glaucoma patients, 404 glaucoma suspects, and 49 healthy controls using commercial OCT device (Cirrus HD-OCT, 200x200 Optic Disc Cubes; Zeiss, Dublin, CA). All subjects had visual field (VF) tests at each visit (Humphrey VF, SITA 24-2 test; Zeiss). A segmentation model was trained using Bayesian deep learning for voxel-wise segmentation of RNFL layer in OCT volume and compute the voxel-wise uncertainty of the segmentation output. The higher uncertainty denotes the unreliability of the segmentation and vice versa and it allows the determination of erroneous segmentation at test time. Uncertainty-guided global mean of the RNLF thickness (RNFL-Umean) was then computed by discarding the voxels with erroneous segmentation labels with higher uncertainty during the thickness computation. Also, the global mean of the RNLF thickness (RNFLmean;) was computed without taking uncertainty into account. Pearson correlation coefficient between RNFLU and MD was computed and compared with the Pearson correlation coefficient between RNFLmean;and MD. Results : The proposed RNFL-Umean;gave stronger correlation with MD than RNFLmean;The Pearson correlation coefficients were (0.67 (RNFL-Umean;) vs 0.63 (RNFL mean;; p<0.001) for glaucoma subjects, (0.56 vs 0.53 ;p=0.01) for glaucoma suspects and (0.08 vs 0.01; p=0.21) for normal subjects. Conclusions : The proposed uncertainty-guided computation of RNFL thickness showed improved correlation with the visual field MD. This demonstrates that segmentation uncertainty can be used to reduce the effect of inaccurate segmentation in computing the RNFL thickness. This also shows that uncertainty-guided computation of RNFL thickness is a better predictor of visual function than the normal RNFL thickness computed without using uncertainty

Purpose : Alterations in retinal oxygen metabolism are implicated in blindness causing diseases, such as diabetic retinopathy and glaucoma. Therefore, a non-invasive clinical tool to assess oxygen saturation (sO2) in retinal vessels is desirable. Recent development of visible-light optical coherence tomography (vis-OCT) enabled non-invasive sO measurements in retinal blood vessels at micrometer-scale resolution by threedimensional (3D) spectroscopic analysis. Nevertheless, such measurements are susceptible to spectral contaminants from the complex retina anatomy and vis-OCT signal detection and processing, decreasing measurement reliability. To overcome limitations posed by spectral contaminants, we developed adaptive-spectroscopic OCT (AS-OCT), a processing technique that enables non-invasive, 3D, environment-independent sO measurements in the human retina. Methods : We used vis-OCT to image the retinas of 18 healthy volunteers. Light exposure in the eye was < 250 muW and imaging acquisition time was 5 sec. We used adaptive spectroscopic OCT (AS-OCT) to identify and remove contaminants from retinal tissues, chromatic aberrations, and spectrally-dependent roll-off. Then, we automatically selected the optimal depths in the vessel for sO measurement. Finally, we measured the attenuation spectrum in the blood vessel and used a least-squares regression fit with known spectra to determine the sO value. Results : We measured sO in 125 unique retinal vessels near the optic disc (vessel diameters ranging from 37 mum to 176 mum). Major arteries had sO2= 97.9 +/-2.9 % (mean +/-standard deviation) (n = 36), small arteries (diameter < 100 had sO2= 93.2 +/-5.0 % (n = 36), and veins had sO2= 58.5 +/-4.3 % (n = 53). Repeated measurement standard deviations were 2.21% and 2.32% for all arteries and veins, respectively. Fig. 1 shows an oximetry map of the optic disc in the retina of a healthy 23 year-old volunteer. Conclusions : AS-OCT enables environment-independent retinal oximetry in the clinical setting. Repeatability in arteries and veins < 2.5 % indicates robust measurements that are promising for clinical use

Purpose : Schlemm canal (SC) is characterized by high local variations in morphology. Previously, we reported characteristics of SC using SC area measurements by optical coherence tomography (OCT) in healthy eyes. Herein, we examine the interobserver variability of SC height, width, and area in glaucomatous and healthy eyes. Methods : The anterior segment of six eyes from three subjects (1 female, 2 male) were imaged using OCT (Cirrus HD-OCT, Zeiss, Dublin, California, USA). A 4x4mm volumetric image of the limbus (depth of 2mm) was acquired with the Anterior Segment Cube scan protocol, comprised of 128 horizontal B-scans composed of 512 A-scans. SC was positioned to the side of the image to maximize visualization of aqueous humor vessel crossings. Scans were processed to maximize visualization of SC; image volumes were averaged (3x3x3 kernel) and contrast was enhanced with the local histogram algorithm using Fiji (version 2.10/1.53c). A cross-sectional B-scan and the two B-scans +/-5 frames were identified as three reference frames, based on best visualized SC location (Fig. 1). Three independent observers performed manual segmentation to measure SC width, height, and cross-sectional area for these three reference frames per volume. Width was defined as the longest measure of SC and height as perpendicular to the line used for width measurement. The observers performed these measurements on 15 volumes for a total of 45 analyzed frames each. The coefficient of variation was calculated based on standard deviations estimated using hierarchical multi-level random-effects models. Interobserver variability was quantified with a two-way ANOVA to calculate the intraclass correlation coefficient (ICC). Results : Participants had a mean age of 72.0 +/- 7.47 years (range: 66 to 82) and consisted of one healthy subject and two with primary open angle glaucoma. Measurement means and variation are presented in Table 1. The ICCs for interobserver variability are excellent for width measurements and low to moderate for height and area (Table 2) Conclusions : Excellent ICC for interobserver variability of SC width suggests it is suitable for use in clinical trials