PURPOSE. To determine which retinal layers are most affected by diabetes and contribute to thinning of the inner retina and to investigate the relationship between retinal layer thickness (LT) and diabetes duration, diabetic retinopathy (DR) status, age, glycosylated hemoglobin (HbA1c), and the sex of the individual, in patients with type 1 diabetes who have no or minimal DR. METHODS. Mean LT was calculated for the individual retinal layers after automated segmentation of spectral domain-optical coherence tomography scans of patients with diabetes and compared with that in control subjects. Multiple linear regression analysis was used to determine the relationship between LT and HbA1c, age, sex, diabetes duration, and DR status. RESULTS. In patients with minimal DR, the mean ganglion cell layer (GCL) in the pericentral area was 5.1 mum thinner (95% confidence interval [CI], 1.1-9.1 mum), and in the peripheral macula, the mean retinal nerve fiber layer (RNFL) was 3.7 mum thinner (95% CI, 1.3-6.1 mum) than in the control subjects. There was a significant linear correlation (R = 0.53, P < 0.01) between GCL thickness and diabetes duration in the pooled group of patients. Multiple linear regression analysis (R = 0.62, P < 0.01) showed that DR status was the most important explanatory variable. CONCLUSIONS. This study demonstrates GCL thinning in the pericentral area and corresponding loss of RNFL thickness in the peripheral macula in patients with type 1 diabetes and no or minimal DR compared with control subjects. These results support the concept that diabetes has an early neurodegenerative effect on the retina, which occurs even though the vascular component of DR is minimal.

Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions—SEADs (Symptomatic Exudate-Associated Derangements)—in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 ± 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and—together with an anatomical atlas of normal retinas—can be used for clinically important applications.

Fully automated 3D segmentation and quantitative analysis of the choroidal vasculature and choriocapillaris-equivalent thickness demonstrated excellent reproducibility in repeat scans (CV 8.0%) and good reproducibility of choriocapillaris-equivalent thickness (CV 27.9%). Our method has the potential to improve the diagnosis and management of patients with eye diseases in which the choroid is affected.

In most eyes, the NCO-based 2D metrics, as estimated by the novel automated graph-theoretic approach to segment the NCO and cup at the level of the RPE/BM complex in SD-OCT volumes, correlate well with RS. However, a small discrepancy exists in NCO-based anatomic structures and the clinical disc margin of the RS in some eyes.

PURPOSE To evaluate the performance of an automated algorithm for determination of the cup and rim from close-to-isotropic spectral domain (SD) OCT images of the optic nerve head (ONH) and compare to the cup and rim as determined by glaucoma experts from stereo color photographs of the same eye. METHODS Thirty-four consecutive patients with glaucoma were included in the study, and the ONH in the left eye was imaged with SD-OCT and stereo color photography on the same day. The cup and rim were segmented in all ONH OCT volumes by a novel voxel column classification algorithm, and linear cup-to-disc (c/d) ratio was determined. Three fellowship-trained glaucoma specialists performed planimetry on the stereo color photographs, and c/d was also determined. The primary outcome measure was the correlation between algorithm-determined c/d and planimetry-derived c/d. RESULTS The correlation of algorithm c/d to experts 1, 2, and 3 was 0.90, 0.87, and 0.93, respectively. The c/d correlation of expert 1 to 2, 1 to 3, and 2 to 3, were 0.89, 0.93, and 0.88, respectively. CONCLUSIONS In this preliminary study, we have developed a novel algorithm to determine the cup and rim in close-to-isotropic SD-OCT images of the ONH and have shown that its performance for determination of the cup and rim from SD-OCT images is similar to that of planimetry by glaucoma experts. Validation on a larger glaucoma sample as well as normal controls is warranted.

PURPOSE. Best disease is a macular dystrophy caused by mutations in the BEST1 gene. Affected individuals exhibit a reduced electro-oculographic (EOG) response to changes in light exposure and have significantly longer outer segments (OS) than age-matched controls. The purpose of this study was to investigate the anatomical changes in the outer retina during dark and light adaptation in unaffected and Best disease subjects, and to compare these changes to the EOG.METHODS. Unaffected (n ¼ 11) and Best disease patients (n ¼ 7) were imaged at approximately 4-minute intervals during an approximately 40-minute dark-light cycle using spectral domain optical coherence tomography (SD-OCT). EOGs of two subjects were obtained under the same conditions. Automated three-dimensional (3-D) segmentation allowed measurement of light-related changes in the distances between five retinal surfaces.RESULTS. In normal subjects, there was a significant decrease in outer segment equivalent length (OSEL) of À2.14 lm (95% confidence interval [CI], À1.77 to À2.51 lm) 10 to 20 minutes after the start of light adaptation, while Best disease subjects exhibited a significant increase in OSEL of 2.07 lm (95% CI, 1.79-2.36 lm). The time course of the change in OS length corresponded to that of the EOG waveform. CONCLUSIONS.Our results strongly suggest that the light peak phase of the EOG is temporally related to a decreased OSEL in normal subjects, and the lack of a light peak phase in Best disease subjects is associated with an increase in OSEL. One potential role of Bestrophin-1 is to trigger an increase in the standing potential that approximates the OS to the apical surface of the RPE to facilitate phagocytosis.

PurposeA 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.MethodsSubjects 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.ResultsWe 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).ConclusionsThe 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.

Automated three-dimensional retinal fluid (named symptomatic exudate-associated derangements, SEAD) segmentation in 3D OCT volumes is of high interest in the improved management of neovascular Age Related Macular Degeneration (AMD). SEAD segmentation plays an important role in the treatment of neovascular AMD, but accurate segmentation is challenging because of the large diversity of SEAD size, location, and shape. Here a novel voxel classification based approach using a layer-dependent stratified sampling strategy was developed to address the class imbalance problem in SEAD detection. The method was validated on a set of 30 longitudinal 3D OCT scans from 10 patients who underwent anti-VEGF treatment. Two retinal specialists manually delineated all intraretinal and subretinal fluid. Leave-one-patient-out evaluation resulted in a true positive rate and true negative rate of 96% and 0.16% respectively. This method showed promise for image guided therapy of neovascular AMD treatment.