Caldendrin represses neurite regeneration and growth in dorsal root ganglion neurons.

Caldendrin is a Ca2+ binding protein that interacts with multiple effectors, such as the Cav1 L-type Ca2+ channel, which play a prominent role in regulating the outgrowth of dendrites and axons (i.e., neurites) during development and in response to injury. Here, we investigated the role of caldendrin in Cav1-dependent pathways that impinge upon neurite growth in dorsal root ganglion neurons (DRGNs). By immunofluorescence, caldendrin was localized in medium- and large- diameter DRGNs. Compared to DRGNs cultured from WT mice, DRGNs of caldendrin knockout (KO) mice exhibited enhanced neurite regeneration and outgrowth. Strong depolarization, which normally represses neurite growth through activation of Cav1 channels, had no effect on neurite growth in DRGN cultures from female caldendrin KO mice. Remarkably, DRGNs from caldendrin KO males were no different from those of WT males in terms of depolarization-dependent neurite growth repression. We conclude that caldendrin opposes neurite regeneration and growth, and this involves coupling of Cav1 channels to growth-inhibitory pathways in DRGNs of females but not males.

Total retinal thickness: a neglected factor in the evaluation of inner retinal thickness.

AIM: To determine whether macular retinal nerve fibre layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thicknesses vary by ethnicity after accounting for total retinal thickness. METHODS: We included healthy participants from the UK Biobank cohort who underwent macula-centred spectral domain-optical coherence tomography scans. mRNFL and GC-IPL thicknesses were determined for groups from different self-reported ethnic backgrounds. Multivariable regression models adjusting for covariables including age, gender, ethnicity and refractive error were built, with and without adjusting for total retinal thickness. RESULTS: 20237 participants were analysed. Prior to accounting for total retinal thickness, mRNFL thickness was on average 0.9 µm (-1.2, -0.6; p<0.001) lower among Asians and 1.5 µm (-2.3, -0.6; p<0.001) lower among black participants compared with white participants. Prior to accounting for total retinal thickness, the average GC-IPL thickness was 1.9 µm (-2.5, -1.4; p<0.001) lower among Asians compared with white participants, and 2.4 µm (-3.9, -1.0; p=0.001) lower among black participants compared with white participants. After accounting for total retinal thickness, the layer thicknesses were not significantly different among ethnic groups. When considered as a proportion of total retinal thickness, mRNFL thickness was ~0.1 and GC-IPL thickness was ~0.2 across age, gender and ethnic groups. CONCLUSIONS: The previously reported ethnic differences in layer thickness among groups are likely driven by differences in total retinal thickness. Our results suggest using layer thickness ratio (retinal layer thicknesses/total retinal thickness) rather than absolute thickness values when comparing retinal layer thicknesses across groups.

Biological Correlations and Confounders for Quantification of Retinal Ganglion Cells by Optical Coherence Tomography Based on Studies of Outbred Mice.

Purpose: Despite popularity of optical coherence tomography (OCT) in glaucoma studies, it's unclear how well OCT-derived metrics compare to traditional measures of retinal ganglion cell (RGC) abundance. Here, Diversity Outbred (J:DO) mice are used to directly compare ganglion cell complex (GCC) thickness measured by OCT to metrics of retinal anatomy measured ex vivo with retinal wholemounts and optic nerve histology. Methods: J:DO mice (n = 48) underwent fundoscopic and OCT examinations, with automated segmentation of GCC thickness. RGC axons were quantified from para-phenylenediamine-stained optic nerve cross-sections and somas from BRN3A-immunolabeled retinal wholemounts, with total inner retinal cellularity assessed by TO-PRO and subsequent hematoxylin staining. Results: J:DO tissues lacked overt disease. GCC thickness, RGC abundance, and total cell abundance varied broadly across individuals. GCC thickness correlated significantly to RGC somal density (r = 0.58) and axon number (r = 0.44), but not total cell density. Retinal area and nerve cross-sectional area varied widely. No metrics were significantly influenced by sex. In bilateral comparisons, GCC thickness (r = 0.95), axon (r = 0.72), and total cell density (r = 0.47) correlated significantly within individuals. Conclusions: Amongst outbred mice, OCT-derived measurements of GCC thickness correlate significantly to RGC somal and axon abundance. Factors limiting correlation are likely both biological and methodological, including differences in retinal area that distort sampling-based estimates of RGC abundance. Translational Relevance: There are significant-but imperfect-correlations between GCC thickness and RGC abundance across genetic contexts in mice, highlighting valid uses and ongoing challenges for meaningful use of OCT-derived metrics.

Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank.

IMPORTANCE: Efforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN). OBJECTIVE: We compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort. DESIGN/SETTING/PARTICIPANTS: Cross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM. EXPOSURE: Diagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%. MAIN OUTCOMES AND MEASURES: Total retinal, mRNFL and GC-IPL thickness. RESULTS: 74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 µm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 µm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 µm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 µm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 µm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 µm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 µm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 µm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness. CONCLUSION: GC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN.

Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.

BACKGROUND: Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). NEW METHOD: Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). RESULTS: The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. CONCLUSIONS: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms.

Automatic control of simulated moving bed process with deep Q-network.

Optimal control of a simulated moving bed (SMB) process is challenging because the system dynamics is represented as nonlinear partial differential-algebraic equations combined with discrete events. In addition, product purity constraints are active at the optimal operating condition, which implies that these constraints can be easily violated by disturbance. Recently, artificial intelligence techniques have received significant attention for their ability to address complex problems, involving a large number of state variables. In this study, a data-based deep Q-network, which is a model-free reinforcement learning method, is applied to the SMB process to train a near-optimal control policy. Using a deep Q-network, the control policy of a complex dynamic system can be trained off-line as long as a sufficient number of data is provided. These data can be efficiently generated by performing numerical simulations in parallel on multiple machines. The on-line computation of the control input using a trained Q-network is fast enough to satisfy the computational time limit for the SMB process. However, because the Q-network does not predict the future state, it is not possible to explicitly impose state constraints. Instead, the state constraints are indirectly imposed by providing a relatively large penalty (negative reward) when the constraints are violate. Furthermore, logic-based switching control is utilized to limit the ranges of the extract and raffinate purities, which helps to satisfy the state constraints and reduce the regions in the state space for reinforcement learning to explore. The simulation results demonstrate the advantages of applying deep reinforcement learning to control the SMB process.

Intravitreal Fluocinolone Acetonide May Decelerate Diabetic Retinal Neurodegeneration.

Purpose: There is no prevention or treatment for diabetic retinal neurodegeneration (DRN), which is a complication of diabetes that can occur independently of diabetic retinopathy (DR). We hypothesized that an intravitreal fluocinolone acetonide (FAc) implant may affect the rate of DRN when used in patients with diabetic macular edema (DME). Methods: In this retrospective analysis, optical coherence tomography with neuroretinal analysis was obtained at 3-month intervals from 130 patients in the USER study both before (mean duration 903 days, range 35-4005 days) and after administration of FAc (mean 408 days, range 7 to 756 days). The rate of DRN was defined as the change over time on inner neuroretinal thickness using logistic regression. A DRN rate was calculated independently for two areas: region 1 located within 1.5 mm of the fovea, and region 2 from 1.5 mm to 3.0 mm from the fovea. Results: In regions of the macula more than 1.5 mm from the central fovea, there was a statistically significant decrease in the rate of DRN in the post-FAc period. The pre-FAc neuroretinal loss in this area occurred at 4.0 µm/y, compared with a post-FAc loss rate of 1.1 µm/y (P = 0.001). Conclusions: This retrospective study suggests that FAc may decelerate the rate of inner retinal thinning in patients with persistent DME. Further prospective studies are necessary to determine the effects of FAc on the rate of DRN in patients with DME.

Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma.

Purpose: A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects. Methods: Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study. Results: We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB). Conclusions: The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.

Nerve Fiber Layer Thickness and Characteristics Associated with Glaucoma in Community Living Older Adults: Prelude to a Screening Trial?

PURPOSE: To examine the associations of nerve fiber layer (NFL) thickness with other ocular characteristics in older adults. METHODS: Participants in the Beaver Dam Eye Study (2008-2010) underwent spectral domain optical coherence tomography (SD-OCT) scans of the optic nerve head, imaging of optic discs, frequency doubling technology (FDT) perimetry, measurement of intraocular pressure (IOP), and an interview concerning their history of glaucoma and use of drops to lower eye pressure. Self-reported histories of glaucoma and the use of drops to lower eye pressure were obtained at follow-up examinations (2014-2016). RESULTS: NFL thickness measured on OCTs varied by location around the optic nerve. Age was associated with mean NFL thickness. Mean NFL was thinnest in eyes with larger cup/disc (C/D) ratios. Horizontal hemifield defects or other optic nerve-field defects were associated with thinner NFL. NFL in persons who reported taking eye drops for high intraocular pressure was thinner compared to those not taking drops. After accounting for the presence of high intraocular pressure, large C/D ratios or hemifield defects, eyes with thinner NFL in the arcades were more likely (OR = 2.3 for 30 micron thinner NFL, p = 0.04) to have incident glaucoma at examination 5 years later. CONCLUSION: Retinal NFL thickness was associated with a new history of self-reported glaucoma 5 years later. A trial testing the usefulness of NFL as part of a screening battery for predicting glaucoma in those previously undiagnosed might lead to improved case finding and, ultimately, to diminishing the risk of visual field loss.

4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans.

Purpose: Longitudinal imaging is becoming more commonplace for studies of disease progression, response to treatment, and healthy maturation. Accurate and reproducible quantification methods are desirable to fully mine the wealth of data in such datasets. However, most current retinal OCT segmentation methods are cross-sectional and fail to leverage the inherent context present in longitudinal sequences of images. Methods: We propose a novel graph-based method for segmentation of multiple three-dimensional (3D) scans over time (termed 3D + time or 4D). The usefulness of this approach in retinal imaging is illustrated in the segmentation of the choroidal surfaces from longitudinal optical coherence tomography (OCT) scans. A total of 3219 synthetic (3070) and patient (149) OCT images were segmented for validation of our approach. Results: The results show that the proposed 4D segmentation method is significantly more reproducible (P < 0.001) than the 3D approach and is significantly more sensitive to temporal changes (P < 0.0001) achieved by the substantial increase of measurement robustness. Conclusions: This is the first automated 4D method for jointly quantifying choroidal thickness in longitudinal OCT studies. Our method is robust to image noise and produces more reproducible choroidal thickness measurements than a sequence of independent 3D segmentations, without sacrificing sensitivity to temporal changes.

Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images.

PURPOSE: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. METHODS: Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm3) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis. RESULTS: The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01). CONCLUSIONS: The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected. TRANSLATIONAL RELEVANCE: Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.

Characterizing the Impact of Off-Axis Scan Acquisition on the Reproducibility of Total Retinal Thickness Measurements in SDOCT Volumes.

PURPOSE: Off-axis acquisition of spectral domain optical coherence tomography (SDOCT) images has been shown to increase total retinal thickness (TRT) measurements. We analyzed the reproducibility of TRT measurements obtained using either the retinal pigment epithelium (RPE) or Bruch's membrane as reference surfaces in off-axis scans intentionally acquired through multiple pupil positions. METHODS: Five volumetric SDOCT scans of the macula were obtained from one eye of 25 normal subjects. One scan was acquired through a central pupil position, while subsequent scans were acquired through four peripheral pupil positions. The internal limiting membrane, the RPE, and Bruch's membrane were segmented using automated approaches. These volumes were registered to each other and the TRT was evaluated in 9 Early Treatment of Diabetic Retinopathy Study (ETDRS) regions. The reproducibility of the TRT obtained using the RPE was computed using the mean difference, coefficient of variation (CV), and the intraclass correlation coefficient (ICC), and compared to those obtained using Bruch's membrane as the reference surface. A secondary set of 1545 SDOCT scans was also analyzed in order to gauge the incidence of off-axis scans in a typical acquisition environment. RESULTS: The photoreceptor tips were dimmer in off-axis images, which affected the RPE segmentation. The overall mean TRT difference and CV obtained using the RPE were 7.04 ± 4.31 µm and 1.46%, respectively, whereas Bruch's membrane was 1.16 ± 1.00 µm and 0.32%, respectively. The ICCs at the subfoveal TRT were 0.982 and 0.999, respectively. Forty-one percent of the scans in the secondary set showed large tilts (> 6%). CONCLUSIONS: RPE segmentation is confounded by its proximity to the interdigitation zone, a structure strongly affected by the optical Stiles-Crawford effect. Bruch's membrane, however, is unaffected leading to a more robust segmentation that is less dependent upon pupil position. TRANSLATIONAL RELEVANCE: The way in which OCT images are acquired can independently affect the accuracy of automated retinal thickness measurements. Assessment of scan angle in a clinical dataset demonstrates that off-axis scans are common, which emphasizes the need for caution when relying on automated thickness parameters when this component of scan acquisition is not controlled for.

Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT.

PURPOSE: To evaluate the validity of a novel fully automated three-dimensional (3D) method capable of segmenting the choroid from two different optical coherence tomography scanners: swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT). METHODS: One hundred eight subjects were imaged using SS-OCT and SD-OCT. A 3D method was used to segment the choroid and quantify the choroidal thickness along each A-scan. The segmented choroidal posterior boundary was evaluated by comparing to manual segmentation. Differences were assessed to test the agreement between segmentation results of the same subject. Choroidal thickness was defined as the Euclidian distance between Bruch's membrane and the choroidal posterior boundary, and reproducibility was analyzed using automatically and manually determined choroidal thicknesses. RESULTS: For SS-OCT, the average choroidal thickness of the entire 6- by 6-mm2 macular region was 219.5 µm (95% confidence interval [CI], 204.9-234.2 µm), and for SD-OCT it was 209.5 µm (95% CI, 197.9-221.0 µm). The agreement between automated and manual segmentations was high: Average relative difference was less than 5 µm, and average absolute difference was less than 15 µm. Reproducibility of choroidal thickness between repeated SS-OCT scans was high (coefficient of variation [CV] of 3.3%, intraclass correlation coefficient [ICC] of 0.98), and differences between SS-OCT and SD-OCT results were small (CV of 11.0%, ICC of 0.73). CONCLUSIONS: We have developed a fully automated 3D method for segmenting the choroid and quantifying choroidal thickness along each A-scan. The method yielded high validity. Our method can be used reliably to study local choroidal changes and may improve the diagnosis and management of patients with ocular diseases in which the choroid is affected.

Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach.

In this work, a multimodal approach is proposed to use the complementary information from fundus photographs and spectral domain optical coherence tomography (SD-OCT) volumes in order to segment the optic disc and cup boundaries. The problem is formulated as an optimization problem where the optimal solution is obtained using a machine-learning theoretical graph-based method. In particular, first the fundus photograph is registered to the 2D projection of the SD-OCT volume. Three in-region cost functions are designed using a random forest classifier corresponding to three regions of cup, rim, and background. Next, the volumes are resampled to create radial scans in which the Bruch's Membrane Opening (BMO) endpoints are easier to detect. Similar to in-region cost function design, the disc-boundary cost function is designed using a random forest classifier for which the features are created by applying the Haar Stationary Wavelet Transform (SWT) to the radial projection image. A multisurface graph-based approach utilizes the in-region and disc-boundary cost images to segment the boundaries of optic disc and cup under feasibility constraints. The approach is evaluated on 25 multimodal image pairs from 25 subjects in a leave-one-out fashion (by subject). The performances of the graph-theoretic approach using three sets of cost functions are compared: 1) using unimodal (OCT only) in-region costs, 2) using multimodal in-region costs, and 3) using multimodal in-region and disc-boundary costs. Results show that the multimodal approaches outperform the unimodal approach in segmenting the optic disc and cup.

Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma.

PURPOSE: To determine relationships between spectral-domain optical coherence tomography (SD-OCT) derived regional damage to the retinal ganglion cell-axonal complex (RGC-AC) and visual thresholds for each location of the Humphrey 24-2 visual field, in all stages of open-angle glaucoma. METHODS: Patients with early, moderate, and advanced glaucoma were recruited from a tertiary glaucoma clinic. Humphrey 24-2 and 9-field Spectralis SD-OCT were acquired for each subject. Individual OCT volumes were aligned, nerve fiber layer (NFL), ganglion cell and inner plexiform layers (GCL+IPL) cosegmented. These layers were then partitioned into 54 sectors corresponding to the 24-2 grid. A Support Vector Machine was trained independently for each sector to predict the sector threshold, using these structural properties. RESULTS: One hundred twenty-two consecutive subjects, 43 early, 39 moderate, and 40 advanced, glaucoma were included (122 eyes). Average correlation coefficient (R) was 0.68 (0.47-0.82), and average root mean square error (RMSE) was 6.92 dB (3.93-8.68 dB). Prediction performance averaged over the entire field, superior hemifield, and inferior hemifield had R (RMSE) values of 0.77 (3.76), 0.80 (5.05), and 0.84 (3.80) dB, respectively. CONCLUSIONS: Predicting individual 24-2 visual field thresholds from structural information derived from nine-field SD-OCT local NFL and GCL+IPL thicknesses using the RGC-AC concept is feasible, showing the potential for the predictive ability of SD-OCT structural information for visual function. Ultimately, it may be feasible to complement and reduce the burden of subjective visual field testing in glaucoma patients with predicted function derived objectively from OCT.

Population-based evaluation of retinal nerve fiber layer, retinal ganglion cell layer, and inner plexiform layer as a diagnostic tool for glaucoma.

PURPOSE: We determined the glaucoma screening performance of regional optical coherence tomography (OCT) layer thickness measurements in the peripapillary and macular region, in a population-based setting. METHODS: Subjects (n = 1224) in the Rotterdam Study underwent visual field testing (Humphrey Field Analyzer) and OCT of the macula and optic nerve head (Topcon 3-D OCT-1000). We determined the mean thicknesses of the retinal nerve fiber layer (RNFL), retinal ganglion cell layer (RGCL), and inner plexiform layer for regions-of-interest; thus, defining a series of OCT parameters, using the Iowa Reference Algorithms. Reference standard was the presence of glaucomatous visual field loss (GVFL); controls were subjects without GVFL, an intraocular pressure (IOP) of 21 mm Hg or less, and no positive family history for glaucoma. We calculated the area under the receiver operating characteristics curve (AUCs) and the sensitivity at 97.5% specificity for each parameter. RESULTS: After excluding 23 subjects with an IOP > 21 mm Hg and 73 subjects with a positive family history for glaucoma, there were 1087 controls and 41 glaucoma cases. Mean RGCL thickness in the inferior half of the macular region showed the highest AUC (0.85; 95% confidence interval [CI] 0.77-0.92) and sensitivity (53.7%; 95% CI, 38.7-68.0%). The mean thickness of the peripapillary RNFL had an AUC of 0.77 (95% CI, 0.69-0.85) and a sensitivity of 24.4% (95% CI, 13.7-39.5%). CONCLUSIONS: Macular RGCL loss is at least as common as peripapillary RNFL abnormalities in population-based glaucoma cases. Screening for glaucoma using OCT-derived regional thickness identifies approximately half of those cases of glaucoma as diagnosed by perimetry.

Subvoxel accurate graph search using non-Euclidean graph space.

Graph search is attractive for the quantitative analysis of volumetric medical images, and especially for layered tissues, because it allows globally optimal solutions in low-order polynomial time. However, because nodes of graphs typically encode evenly distributed voxels of the volume with arcs connecting orthogonally sampled voxels in Euclidean space, segmentation cannot achieve greater precision than a single unit, i.e. the distance between two adjoining nodes, and partial volume effects are ignored. We generalize the graph to non-Euclidean space by allowing non-equidistant spacing between nodes, so that subvoxel accurate segmentation is achievable. Because the number of nodes and edges in the graph remains the same, running time and memory use are similar, while all the advantages of graph search, including global optimality and computational efficiency, are retained. A deformation field calculated from the volume data adaptively changes regional node density so that node density varies with the inverse of the expected cost. We validated our approach using optical coherence tomography (OCT) images of the retina and 3-D MR of the arterial wall, and achieved statistically significant increased accuracy. Our approach allows improved accuracy in volume data acquired with the same hardware, and also, preserved accuracy with lower resolution, more cost-effective, image acquisition equipment. The method is not limited to any specific imaging modality and readily extensible to higher dimensions.

Reproducibility of SD-OCT-based ganglion cell-layer thickness in glaucoma using two different segmentation algorithms.

PURPOSE: To compare the reproducibility of spectral-domain optical coherence tomography (SD-OCT)-based ganglion cell-layer-plus-inner plexiform-layer (GCL+IPL) thickness measurements for glaucoma patients obtained using both a publicly available and a commercially available algorithm. METHODS: Macula SD-OCT volumes (200 × 200 × 1024 voxels, 6 × 6 × 2 mm(3)) were obtained prospectively from both eyes of patients with open-angle glaucoma or with suspected glaucoma on two separate visits within 4 months. The combined GCL+IPL thickness was computed for each SD-OCT volume within an elliptical annulus centered at the fovea, based on two algorithms: (1) a previously published graph-theoretical layer segmentation approach developed at the University of Iowa, and (2) a ganglion cell analysis module of version 6 of Cirrus software. The mean overall thickness of the elliptical annulus was computed as was the thickness within six sectors. For statistical analyses, eyes with an SD-OCT volume with low signal strength (<6), image acquisition errors, or errors in performing the commercial GCL+IPL analysis in at least one of the repeated acquisitions were excluded. RESULTS: Using 104 eyes (from 56 patients) with repeated measurements, we found the intraclass correlation coefficient for the overall elliptical annular GCL+IPL thickness to be 0.98 (95% confidence interval [CI]: 0.97-0.99) with the Iowa algorithm and 0.95 (95% CI: 0.93-0.97) with the Cirrus algorithm; the intervisit SDs were 1.55 µm (Iowa) and 2.45 µm (Cirrus); and the coefficients of variation were 2.2% (Iowa) and 3.5% (Cirrus), P < 0.0001. CONCLUSIONS: SD-OCT-based GCL+IPL thickness measurements in patients with early glaucoma are highly reproducible.

Adjustment of the retinal angle in SD-OCT of glaucomatous eyes provides better intervisit reproducibility of peripapillary RNFL thickness.

PURPOSE: To report an automated method for adjustment of the retinal angle in spectral-domain optical coherence tomography (SD-OCT) and compare its intervisit reproducibility of the peripapillary retinal nerve fiber layer (RNFL) thicknesses of glaucomatous eyes to that obtained by the Cirrus algorithm. METHODS: Fifty-six glaucoma and glaucoma suspect subjects were repeatedly imaged, and optic nerve head (ONH)-centered OCT image volumes (200×200×1024 voxels, 6×6×2 mm3, Cirrus HD-OCT machine) were acquired within a 4-month period from one eye of the 56 patients. Retinal angle correction in B-scans was accomplished by adjusting the angle using the voxel aspect ratio of the SD-OCT followed by straightening of rotated A-scans. The RNFL layer was automatically segmented using the Iowa Reference Algorithm. Reproducibility of the peripapillary RNFL thicknesses was determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), repeatability coefficient (RC), and 95% tolerance limit (TL) for the Iowa Reference Algorithm without and with the retinal angle correction and for the Cirrus algorithm (Cirrus version 5.1.0.96). RESULTS: The angle corrected Iowa Reference Algorithm (ICC: 0.990, 95% confidence interval [CI]: 0.983-0.994) for peripapillary RNFL thicknesses showed significantly better reproducibility than the nonangle corrected algorithm (ICC: 0.964, 95% CI: 0.940-0.979) and the Cirrus algorithm (ICC: 0.960, 95% CI: 0.933-0.976) based on the 95% CIs for the ICCs. CONCLUSIONS: Angle correction leads to more consistent peripapillary RNFL thicknesses. This may lead to improved management of patients with glaucoma.

Reproducibility of diabetic macular edema estimates from SD-OCT is affected by the choice of image analysis algorithm.

PURPOSE: To evaluate the intersession repeatability of retinal thickness measurements in patients with diabetic macular edema (DME) using the Heidelberg Spectralis optical coherence tomography (OCT) algorithm and a publicly available, three-dimensional graph search-based multilayer OCT segmentation algorithm, the Iowa Reference Algorithm. METHODS: Thirty eyes from 21 patients diagnosed with clinically significant DME were included and underwent consecutive, registered macula-centered spectral-domain optical coherence scans (Heidelberg Spectralis). The OCT scans were segmented into separate surfaces, and the average thickness between internal limiting membrane and outer retinal pigment epithelium complex surfaces was determined using the Iowa Reference Algorithm. Variability between paired scans was analyzed and compared with the retinal thickness obtained from the manufacturer-supplied Spectralis software. RESULTS: The coefficient of repeatability (variation) for central macular thickness using the Iowa Reference Algorithm was 5.26 µm (0.62% [95% confidence interval (CI), 0.43-0.71]), while for the Spectralis algorithm this was 6.84 µm (0.81% [95% CI, 0.55-0.92]). When the central 3 mm was analyzed, the coefficient of repeatability (variation) was 2.46 µm (0.31% [95% CI, 0.23-0.38]) for the Iowa Reference Algorithm and 4.23 µm (0.53% [95% CI, 0.39-0.65]) for the Spectralis software. CONCLUSIONS: The Iowa Reference Algorithm and the Spectralis software provide excellent reproducibility between serial scans in patients with clinically significant DME. The publicly available Iowa Reference Algorithm may have lower between-measurement variation than the manufacturer-supplied Spectralis software for the central 3 mm subfield. These findings have significant implications for the management of patients with DME.