DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction.

Dense pixel matching problems such as optical flow and disparity estimation are among the most challenging tasks in computer vision. Recently, several deep learning methods designed for these problems have been successful. A sufficiently larger effective receptive field (ERF) and a higher resolution of spatial features within a network are essential for providing higher-resolution dense estimates. In this work, we present a systemic approach to design network architectures that can provide a larger receptive field while maintaining a higher spatial feature resolution. To achieve a larger ERF, we utilized dilated convolutional layers. By aggressively increasing dilation rates in the deeper layers, we were able to achieve a sufficiently larger ERF with a significantly fewer number of trainable parameters. We used optical flow estimation problem as the primary benchmark to illustrate our network design strategy. The benchmark results (Sintel, KITTI, and Middlebury) indicate that our compact networks can achieve comparable performance in the class of lightweight networks.

Dense optic nerve head deformation estimated using CNN as a structural biomarker of glaucoma progression.

PURPOSE: To present a new structural biomarker for detecting glaucoma progression based on structural transformation of the optic nerve head (ONH) region over time. METHODS: Dense ONH deformation was estimated using deep learning methods namely DDCNet-Multires, FlowNet2, and FlowNetCorrelation, and legacy computational methods namely the topographic change analysis (TCA) and proper orthogonal decomposition (POD) methods. A candidate biomarker was estimated as the average magnitude of deformation of the ONH and evaluated using longitudinal confocal scans of 12 laser treated and 12 contralateral normal eyes of 12 primates from the LSU Experimental Glaucoma Study (LEGS); and 36 progressing eyes and 21 longitudinal normal eyes from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Area under the ROC curve (AUC) was used to assess the diagnostic accuracy of the biomarker. RESULTS: AUROC (95% CI) for LEGS were: 0.83 (0.79, 0.88) for DDCNet-Multires; 0.83 (0.78, 0.88) for FlowNet2; 0.83 (0.78, 0.88) for FlowNet-Correlation; 0.94 (0.91, 0.97) for POD; and 0.86 (0.82, 0.91) for TCA methods. For DIGS: 0.89 (0.80, 0.97) for DDCNet-Multires; 0.82 (0.71, 0.93) for FlowNet2; 0.93 (0.86, 0.99) for FlowNet-Correlation; 0.86 (0.76, 0.96) for POD; and 0.86 (0.77, 0.95) for TCA methods. Lower diagnostic accuracy of the learning-based methods for LEG study eyes were due to image alignment errors in confocal sequences. CONCLUSION: Deep learning methods trained to estimate generic deformation were able to estimate ONH deformation from image sequences and provided a higher diagnostic accuracy. Our validation of the biomarker using ONH sequences from controlled experimental conditions confirms the diagnostic accuracy of the biomarkers observed in the clinical population. Performance can be further improved by fine-tuning these networks using ONH sequences.

A novel factor graph-based optimization technique for stereo correspondence estimation.

Dense disparities among multiple views are essential for estimating the 3D architecture of a scene based on the geometrical relationship between the scene and the views or cameras. Scenes with larger extents of homogeneous textures, differing scene illumination among the multiple views and with occluding objects affect the accuracy of the estimated disparities. Markov random fields based methods for disparity estimation address these limitations using spatial dependencies among the observations and among the disparity estimates. These methods, however, are limited by spatially fixed and smaller neighborhood systems or cliques. Recent learning-based methods generate rich set of stereo features for generating cost volume and estimating disparity. In this work, we present a new factor graph-based probabilistic graphical model for disparity estimation that allows a larger and a spatially variable neighborhood structure determined based on the local scene characteristics. Our algorithm improves the accuracy of disparity estimates in stereo image pairs with varying texture and illumination characteristics by enforcing spatial dependencies among scene characteristics as well as among disparity estimates. We evaluated our method using the Middlebury benchmark stereo datasets and the Middlebury evaluation dataset version 3.0 and compared its performance with recent state-of-the-art disparity estimation algorithms. Our factor graph-based algorithm provided disparity estimates with higher accuracy when compared to the recent non-learning- and learning-based disparity estimation algorithms. The factor graph formulation can be used for obtaining maximum a posteriori estimates from models or optimization problems with complex dependency structure among hidden variables. The strategies of using a priori distributions with shorter support and spatial dependencies were useful for reducing the computational cost and improving message convergence in the model. The factor-graph algorithm is also useful for other dense estimation problems such as optical flow estimation.

A Novel Nanoparticle Mediated Selective Inner Retinal Photocoagulation for Diseases of the Inner Retina.

A novel nanoparticle mediated methodology for laser photocoagulation of the inner retina to achieve tissue selective treatment is presented. METHODS: Transport of 527, 577, and 810 nm laser, heat deposition, and eventual thermal damage in vitreous, retina, RPE, choroid, and sclera were modeled using Bouguer-Beer-Lambert law of absorption and solved numerically using the finite volume method. Nanoparticles were designed using Mie theory of scattering. Performance of the new photocoagulation strategy using gold nanospheres and gold-silica nanoshells was compared with that of conventional methods without nanoparticles. For experimental validation, vitreous cavity of ex vivo porcine eyes was infused with gold nanospheres. After ~6 h of nanoparticle diffusion, the porcine retina was irradiated with a green laser and imaged simultaneously using a spectral domain optical coherence tomography (Spectralis SD-OCT, Heidelberg Engineering). RESULTS: Our computational model predicted a significant spatial shift in the peak temperature from RPE to the inner retinal region when infused with nanoparticles. Arrhenius thermal damage in the mid-retinal location was achieved in ~14 ms for 527 nm laser thereby reducing the irradiation duration by ~30 ms compared with the treatment without nanoparticles. In ex vivo porcine eyes infused with gold nanospheres, SD-OCT retinal images revealed a lower thermal damage and expansion at RPE due to laser photocoagulation. CONCLUSION: Nanoparticle infused laser photocoagulation strategy provided a selective inner retinal thermal damage with significant decrease in laser power and laser exposure time. SIGNIFICANCE: The proposed treatment strategy shows possibilities for an efficient and highly selective inner retinal laser treatment.

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

PURPOSE: To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. METHODS: GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). RESULTS: Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. CONCLUSIONS: GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. TRANSLATIONAL RELEVANCE: Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.

UNLABELLED: Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases. METHOD: This paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class. RESULTS: The proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively). CONCLUSION: The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection.

Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points.

Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests. Using the collected data, a longitudinal feature vector was created for each patient's eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit. The longitudinal features from each patient's eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time. This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families. Combinations of structural and functional features were selected and ranked to determine the relative effectiveness of each feature. Finally, the outcomes of the classifiers were assessed by several performance metrics and the effectiveness of structural and functional features were analyzed.

Detecting glaucoma progression from localized rates of retinal changes in parametric and nonparametric statistical framework with type I error control.

PURPOSE: We evaluated three new pixelwise rates of retinal height changes (PixR) strategies to reduce false-positive errors while detecting glaucomatous progression. METHODS: Diagnostic accuracy of nonparametric PixR-NP cluster test (CT), PixR-NP single threshold test (STT), and parametric PixR-P STT were compared to statistic image mapping (SIM) using the Heidelberg Retina Tomograph. We included 36 progressing eyes, 210 nonprogressing patient eyes, and 21 longitudinal normal eyes from the University of California, San Diego (UCSD) Diagnostic Innovations in Glaucoma Study. Multiple comparison problem due to simultaneous testing of retinal locations was addressed in PixR-NP CT by controlling family-wise error rate (FWER) and in STT methods by Lehmann-Romano's k-FWER. For STT methods, progression was defined as an observed progression rate (ratio of number of pixels with significant rate of decrease; i.e., red-pixels, to disk size) > 2.5%. Progression criterion for CT and SIM methods was presence of one or more significant (P < 1%) red-pixel clusters within disk. RESULTS: Specificity in normals: CT = 81% (90%), PixR-NP STT = 90%, PixR-P STT = 90%, SIM = 90%. Sensitivity in progressing eyes: CT = 86% (86%), PixR-NP STT = 75%, PixR-P STT = 81%, SIM = 39%. Specificity in nonprogressing patient eyes: CT = 49% (55%), PixR-NP STT = 56%, PixR-P STT = 50%, SIM = 79%. Progression detected by PixR in nonprogressing patient eyes was associated with early signs of visual field change that did not yet meet our definition of glaucomatous progression. CONCLUSIONS: The PixR provided higher sensitivity in progressing eyes and similar specificity in normals than SIM, suggesting that PixR strategies can improve our ability to detect glaucomatous progression. Longer follow-up is necessary to determine whether nonprogressing eyes identified as progressing by these methods will develop glaucomatous progression. (ClinicalTrials.gov number, NCT00221897).

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

PURPOSE: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. METHODS: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. RESULTS: FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. CONCLUSIONS: VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

Glaucoma progression detection using nonlocal Markov random field prior.

Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

PURPOSE: We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. METHODS: POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. RESULTS: POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). CONCLUSIONS: POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

Localized glaucomatous change detection within the proper orthogonal decomposition framework.

PURPOSE: To detect localized glaucomatous structural changes using proper orthogonal decomposition (POD) framework with false-positive control that minimizes confirmatory follow-ups, and to compare the results to topographic change analysis (TCA). METHODS: We included 167 participants (246 eyes) with ≥4 Heidelberg Retina Tomograph (HRT)-II exams from the Diagnostic Innovations in Glaucoma Study; 36 eyes progressed by stereo-photographs or visual fields. All other patient eyes (n = 210) were non-progressing. Specificities were evaluated using 21 normal eyes. Significance of change at each HRT superpixel between each follow-up and its nearest baseline (obtained using POD) was estimated using mixed-effects ANOVA. Locations with significant reduction in retinal height (red pixels) were determined using Bonferroni, Lehmann-Romano k-family-wise error rate (k-FWER), and Benjamini-Hochberg false discovery rate (FDR) type I error control procedures. Observed positive rate (OPR) in each follow-up was calculated as a ratio of number of red pixels within disk to disk size. Progression by POD was defined as one or more follow-ups with OPR greater than the anticipated false-positive rate. TCA was evaluated using the recently proposed liberal, moderate, and conservative progression criteria. RESULTS: Sensitivity in progressors, specificity in normals, and specificity in non-progressors, respectively, were POD-Bonferroni = 100%, 0%, and 0%; POD k-FWER = 78%, 86%, and 43%; POD-FDR = 78%, 86%, and 43%; POD k-FWER with retinal height change ≥50 µm = 61%, 95%, and 60%; TCA-liberal = 86%, 62%, and 21%; TCA-moderate = 53%, 100%, and 70%; and TCA-conservative = 17%, 100%, and 84%. CONCLUSIONS: With a stronger control of type I errors, k-FWER in POD framework minimized confirmatory follow-ups while providing diagnostic accuracy comparable to TCA. Thus, POD with k-FWER shows promise to reduce the number of confirmatory follow-ups required for clinical care and studies evaluating new glaucoma treatments. (ClinicalTrials.gov number, NCT00221897.).

Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.

PURPOSE: The goal of this study was to determine if glaucomatous progression in suspect eyes can be predicted from baseline confocal scanning laser ophthalmoscope (CSLO) and standard automated perimetry (SAP) measurements analyzed with relevance vector machine (RVM) classifiers. METHODS: Two hundred sixty-four eyes of 193 participants were included. All eyes had normal SAP results at baseline with five or more SAP tests over time. Eyes were labeled progressed (n = 47) or stable (n = 217) during follow-up based on SAP Guided Progression Analysis or serial stereophotograph assessment. Baseline CSLO-measured topographic parameters (n = 117) and baseline total deviation values from the 24-2 SAP test-grid (n = 52) were selected from each eye. Ten-fold cross-validation was used to train and test RVMs using the CSLO and SAP features. Receiver operating characteristic (ROC) curve areas were calculated using full and optimized feature sets. ROC curve results from RVM analyses of CSLO, SAP, and CSLO and SAP combined were compared to CSLO and SAP global indices (Glaucoma Probability Score, mean deviation and pattern standard deviation). RESULTS: The areas under the ROC curves (AUROCs) for RVMs trained on optimized feature sets of CSLO parameters, SAP parameters, and CSLO and SAP parameters combined were 0.640, 0.762, and 0.805, respectively. AUROCs for CSLO Glaucoma Probability Score, SAP mean deviation (MD), and SAP pattern standard deviation (PSD) were 0.517, 0.513, and 0.620, respectively. No CSLO or SAP global indices discriminated between baseline measurements from progressed and stable eyes better than chance. CONCLUSIONS: In our sample, RVM analyses of baseline CSLO and SAP measurements could identify eyes that showed future glaucomatous progression with a higher accuracy than the CSLO and SAP global indices. (ClinicalTrials.gov numbers, NCT00221897, NCT00221923.).

Agreement between the Heidelberg Retina Tomograph (HRT) stereometric parameters estimated using HRT-I and HRT-II.

PURPOSE: To assess agreement between Heidelberg Retina Tomograph (HRT)-I and HRT-II stereometric parameters and to determine whether parabolic error correction (PEC) to the topographies improves agreement. METHODS: University of California San Diego Diagnostic Innovations in Glaucoma Study participants with two HRT-II examinations (n = 380) or one HRT-I and one HRT-II examinations (n = 344) acquired on the same day were included. From the group of 380 eyes, 200 eyes were randomly selected to estimate the repeatability coefficients of HRT-II rim area and volume, cup area and volume, and mean retinal nerve fiber layer (RNFL) thickness parameters (HRT-II control group), and the remaining 180 eyes were used to assess agreement between two HRT-II examinations (HRT-II study group). Agreement between stereometric parameters of HRT-I and HRT-II examinations (HRT-I vs. HRT-II study group) were assessed with (1) no PEC, (2) HRT PEC, and (3) a modified PEC. Bland-Altman plots were used to assess agreement using estimates of bias and clinical limits of agreement (CLA) based on repeatability coefficients. RESULTS: In the HRT-II study group, agreement between stereometric parameters was good, with no statistically significant biases. For all parameters, differences were within the CLA in 94% of participants. In the HRT-I vs. HRT-II study group, there was a small statistically significant bias between the stereometric parameters, but all differences were within CLA for ≥95% of participants. In both study groups, PEC did not improve agreement. CONCLUSIONS: Agreement between HRT-I and HRT-II stereometric parameters was good, and PEC did not improve agreement. These results suggest that HRT-I and HRT-II examinations can be used interchangeably to detect changes in stereometric parameters over time.

Determinants of agreement between the confocal scanning laser tomograph and standardized assessment of glaucomatous progression.

PURPOSE: To estimate the agreement of confocal scanning laser tomograph (CSLT), topographic change analysis (TCA) with assessment of stereophotographs, and standard automated perimetry (SAP) for detecting glaucomatous progression and to identify factors associated with agreement between methods. DESIGN: Observational cohort study. PARTICIPANTS: We included 246 eyes of 167 glaucoma patients, glaucoma suspects, and ocular hypertensives. METHODS: We included CSLT series (n ≥ 4 tests; mean follow-up, 4 years), stereophotographs, and SAP results in the analysis. The number of progressors by guided progression analysis (GPA, "likely progression"), progressors by masked stereophotographs assessment and progressors by TCA as determined for 3 parameters related to the number of progressed superpixels within the disc margin was determined. Agreement between progression by each TCA parameter, stereophotographs and GPA was assessed using the Kappa test. Analysis of variance with post hoc analysis was applied to identify baseline factors including image quality (standard deviation of the mean topography), disc size and disease severity (pattern standard deviation [PSD] and cup area) associated with agreement/nonagreement between methods. MAIN OUTCOME MEASURES: Agreement in assessing glaucomatous progression between the methods including factors associated with agreement/nonagreement between methods. RESULTS: Agreement between progression by TCA and progression by stereophotographs and/or GPA was generally poor regardless of the TCA parameter and specificity cutoffs applied. For the parameters with the strongest agreement, cluster size in disc (CSIZE(disc)) and cluster area in disc (CAREA(disc)), kappa values were 0.16 (63.9%, agreement on 134 nonprogressing eyes and 23 progressing eyes) and 0.15 (64.1%, agreement on 135 nonprogressing eyes and 22 progressing eyes) at 99% cutoff. Most of the factors evaluated were not significantly associated with agreement/nonagreement between methods (all P > 0.07). However, SAP PSD was greater in the progressors by stereophotography only group compared with the progressors by TCA only group (5.8 ± 4.7 and 2.6 ± 2.2, respectively [P = 0.003] for CSIZE(disc) at 95% specificity and 5.4 ± 4.6 and 2.5 ± 2.3, respectively [P = 0.002] for CAREA(disc) at 99% specificity). CONCLUSIONS: Agreement for detection of longitudinal changes between TCA, stereophotography, and SAP GPA is poor. Progressors by stereophotography only tended to have more advanced disease at baseline than progressors by TCA only.

Clinical evaluation of the proper orthogonal decomposition framework for detecting glaucomatous changes in human subjects.

PURPOSE: To evaluate the new proper orthogonal decomposition (POD) framework for detecting glaucomatous progression from HRT topographies of human subjects and compare it with HRT topographic change analysis (TCA). METHODS: Of 267 eyes of 187 participants with > or =4 retinal tomographic examinations in the University of California, San Diego Diagnostic Innovations in Glaucoma Study (DIGS), 21 eyes were of longitudinally normal subjects and 36 eyes progressed by stereophotographs or visual field-guided progression analysis (progressors). All others were considered nonprogressing (nonprogressors; n = 210 eyes). POD parameters of Euclidean distance (L(2) norm), image Euclidean distance, and correlation were computed, and their area under receiver operating characteristic curves (AUC) in differentiating progressors from nonprogressors and normal subjects were compared to the TCA parameters of the number of superpixels with significant decrease in retinal height (red pixels), size of the largest cluster of red pixels (CSIZE), and CSIZE% of disc size, all within the optic disc margin. RESULTS: AUCs of the best performing POD L(2) norm and TCA red pixel parameters in differentiating progressors from normal subjects were both 0.86 and in differentiating progressors from nonprogressors were 0.68 and 0.64, respectively; the AUC differences were not statistically significant. CONCLUSIONS: The POD framework, which can detect and confirm glaucomatous changes in a single follow-up visit, provides a performance similar to that of TCA in differentiating progressors from normal subjects and nonprogressors.

A framework for detecting glaucomatous progression in the optic nerve head of an eye using proper orthogonal decomposition.

Glaucoma is the second leading cause of blindness worldwide. Often, the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this paper, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L1-norm and L2 -norm, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUCs) was used to compare the diagnostic performance of the POD-induced parameters with the parameters of the topographic change analysis (TCA) method. The IMED and L2-norm parameters in the POD framework provided the highest AUC of 0.94 at 10 degrees field of imaging and 0.91 at 15 degrees field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88, respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management.

Spectral domain-optical coherence tomography to detect localized retinal nerve fiber layer defects in glaucomatous eyes.

This study examines the ability of RTVue, Cirrus and Spectralis OCT Spectral domain-optical coherence tomographs (SD-OCT) to detect localized retinal nerve fiber layer defects in glaucomatous eyes. In this observational case series, four glaucoma patients (8 eyes) were selected from the University of California, San Diego Shiley Eye Center and the Diagnostic Innovations in Glaucoma Study (DIGS) based on the presence of documented localized RNFL defects in at least one eye confirmed by masked stereophotograph assessment. One RTVue 3D Disc scan, one RTVue NHM4 scan, one Cirrus Optic Disk Cube 200x200 scan and one Spectralis scan centered on the optic disc (15x15 scan angle, 768 A-scans x 73 B-scans) were obtained on all undilated eyes within a single session. Results were compared with those obtained from stereophotographs. In 6 eyes the presence of localized RNFL defects was detected by stereophotography. In general, by qualitatively evaluating the retinal thickness maps generated, all SD-OCT instruments examined were able to confirm the presence of localized glaucomatous structural damage seen on stereophotographs. This study confirms SD-OCT is a promising technology for glaucoma detection as it may assist clinicians identify the presence of localized glaucomatous structural damage.