Kyungmoo Lee

Decreased Retinal Ganglion Cell Layer Thickness in Patients with Type 1 Diabetes

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.

Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula

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.

Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head

Glaucoma is the second leading ocular disease causing blindness due to gradual damage to the optic nerve and resultant visual field loss. Segmentations of the optic disc cup and neuroretinal rim can provide important parameters for detecting and tracking this disease. The purpose of this study is to describe and evaluate a method that can automatically segment the optic disc cup and rim in spectral-domain 3-D OCT (SD-OCT) volumes. Four intraretinal surfaces were segmented using a fast multiscale 3-D graph search algorithm. After surface segmentation, the retina in each 3-D OCT scan was flattened to ensure a consistent optic nerve head shape. A set of 15 features, derived from the segmented intraretinal surfaces and voxel intensities in the SD-OCT volume, were used to train a classifier that can determine which A-scans in the OCT volume belong to the background, optic disc cup and rim. Finally, prior knowledge about the shapes of the cup and rim was incorporated into the system using a convex hull-based approach. Two glaucoma experts annotated the cup and rim area using planimetry, and the annotations of the first expert were used as the reference standard. A leave-one-subject-out experiment on 27 optic nerve head-centered OCT volumes (14 right eye scans and 13 left eye scans from 14 patients) was performed. Two different types of classification methods were compared, and experimental results showed that the best performing method had an unsigned error for the optic disc cup of 2.52 ? 0.87 pixels (0.076 ? 0.026 mm) and for the neuroretinal rim of 2.04 ? 0.86 pixels (0.061 ? 0.026 mm). The interobserver variability as indicated by the unsigned border positioning difference between the second expert observer and the reference standard was 2.54 ? 1.03 pixels (0.076 ? 0.031 mm for the optic disc cup and 2.14 ? 0.80 pixels (0.064 ? 0.024 mm for the neuroretinal rim. The unsigned error of the best performing method was not significantly different (p > 0.2) from the interobserver variability.

Three-Dimensional Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search-Graph-Cut

An automated method is reported for segmenting 3-D fluid-associated abnormalities in the retina, so-called symptomatic exudate-associated derangements (SEAD), from 3-D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD regions identified, and the retinal OCT image is flattened using a candidate-SEAD aware approach. In the second stage, a probability constrained combined graph search-graph cut method refines the candidate SEADs by integrating the candidate volumes into the graph cut cost function as probability constraints. The proposed method was evaluated on 15 spectral domain OCT images from 15 subjects undergoing intravitreal anti-VEGF injection treatment. Leave-one-out evaluation resulted in a true positive volume fraction (TPVF), false positive volume fraction (FPVF) and relative volume difference ratio (RVDR) of 86.5%, 1.7%, and 12.8%, respectively. The new graph cut-graph search method significantly outperformed both the traditional graph cut and traditional graph search approaches (p <; 0.01, p <; 0.04) and has the potential to improve clinical management of patients with choroidal neovascularization due to exudative age-related macular degeneration.

Automated Segmentation of the Choroid from Clinical SD-OCT

PURPOSE We developed and evaluated a fully automated 3-dimensional (3D) method for segmentation of the choroidal vessels, and quantification of choroidal vasculature thickness and choriocapillaris-equivalent thickness of the macula, and evaluated repeat variability in normal subjects using standard clinically available spectral domain optical coherence tomography (SD-OCT). METHODS A total of 24 normal subjects was imaged twice, using clinically available, 3D SD-OCT. A novel, fully-automated 3D method was used to segment and visualize the choroidal vasculature in macular scans. Local choroidal vasculature and choriocapillaris-equivalent thicknesses were determined. Reproducibility on repeat imaging was analyzed using overlapping rates, Dice coefficient, and root mean square coefficient of variation (CV) of choroidal vasculature and choriocapillaris-equivalent thicknesses. RESULTS For the 6 × 6 mm(2) macula-centered region as depicted by the SD-OCT, average choroidal vasculature thickness in normal subjects was 172.1 μm (95% confidence interval [CI] 163.7-180.5 μm) and average choriocapillaris-equivalent thickness was 23.1 μm (95% CI 20.0-26.2 μm). Overlapping rates were 0.79 ± 0.07 and 0.75 ± 0.06, Dice coefficient was 0.78 ± 0.08, CV of choroidal vasculature thickness was 8.0% (95% CI 6.3%-9.4%), and of choriocapillaris-equivalent thickness was 27.9% (95% CI 21.0%-33.3%). CONCLUSIONS 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.

Automated Segmentation of the Cup and Rim from Spectral Domain OCT of the Optic Nerve Head

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.

Automated segmentation of neural canal opening and optic cup in 3D spectral optical coherence tomography volumes of the optic nerve head

PURPOSE To develop an automated approach for segmenting the neural canal opening (NCO) and cup at the level of the retinal pigment epithelium (RPE)/Bruchs membrane (BM) complex in spectral-domain optical coherence tomography (SD-OCT) volumes. To investigate the correspondence and discrepancy between the NCO-based metrics and the clinical disc margin on fundus photographs of glaucoma subjects. METHODS SD-OCT scans and corresponding stereo fundus photographs of the optic nerve head were obtained from 68 eyes of 34 patients with glaucoma or glaucoma suspicion. Manual planimetry was performed by three glaucoma experts to delineate a reference standard (RS) for cup and disc margins from the images. An automated graph-theoretic approach was used to identify the NCO and cup. NCO-based metrics were compared with the RS. RESULTS Compared with the RS disc margin, the authors found mean unsigned and signed border differences of 2.81 ± 1.48 pixels (0.084 ± 0.044 mm) and -0.99 ± 2.02 pixels (-0.030 ± 0.061 mm), respectively, for NCO segmentation. The correlations of the linear cup-to-disc (NCO) area ratio, disc (NCO) area, rim area, and cup area of the algorithm with the RS were 0.85, 0.77, 0.69, and 0.83, respectively. CONCLUSIONS 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.

Registration of 3D spectral OCT volumes using 3D SIFT feature point matching

The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image registration is a vital tool to enable more precise, quantitative comparison of disease states. This work presents a registration method based on a recently introduced extension of the 2D Scale-Invariant Feature Transform (SIFT) framework1 to 3D.2 The SIFT feature extractor locates minima and maxima in the difference of Gaussian scale space to find salient feature points. It then uses histograms of the local gradient directions around each found extremum in 3D to characterize them in a 4096 element feature vector. Matching points are found by comparing the distance between feature vectors. We apply this method to the rigid registration of optic nerve head- (ONH) and macula-centered 3D OCT scans of the same patient that have only limited overlap. Three OCT data set pairs with known deformation were used for quantitative assessment of the methods robustness and accuracy when deformations of rotation and scaling were considered. Three-dimensional registration accuracy of 2.0±3.3 voxels was observed. The accuracy was assessed as average voxel distance error in N=1572 matched locations. The registration method was applied to 12 3D OCT scans (200 x 200 x 1024 voxels) of 6 normal eyes imaged in vivo to demonstrate the clinical utility and robustness of the method in a real-world environment.

Human photoreceptor outer segments shorten during light adaptation.

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 = 11) and Best disease patients (n = 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 -2.14 μm (95% confidence interval [CI], -1.77 to -2.51 μm) 10 to 20 minutes after the start of light adaptation, while Best disease subjects exhibited a significant increase in OSEL of 2.07 μm (95% CI, 1.79-2.36 μm). 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.

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 Bruchs 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.