We developed a method to normalize optical coherence tomography (OCT) signal profiles from two spectral-domain (SD) OCT devices so that the comparability between devices increases.
We scanned 21 eyes from 14 healthy and 7 glaucoma subjects with two SD-OCT devices on the same day, with equivalent cube scan patterns centered on the fovea (Cirrus HD-OCT and RTVue). Foveola positions were selected manually and used as the center for registration of the corresponding images. A-scan signals were sampled 1.8 mm from the foveola in the temporal, superior, nasal, and inferior quadrants. After oversampling and rescaling RTVue data along the Z-axis to match the corresponding Cirrus data format, speckle noise reduction and amplitude normalization were applied. For comparison between normalized A-scan profiles, mean absolute difference in amplitude in percentage was measured at each sampling point. As a reference, the mean absolute difference between two Cirrus scans on the same eye also was measured.
The mean residual of the A-scan profile amplitude was reduced significantly after signal normalization (12.7% vs. 6.2%, P < 0.0001, paired t-test). All four quadrants also showed statistically significant reduction (all P < 0.0001). Mean absolute difference after normalization was smaller than the one between two Cirrus scans. No performance difference was detected between health and glaucomatous eyes.
The reported signal normalization method successfully reduced the A-scan profile differences between two SD-OCT devices. This signal normalization processing may improve the direct comparability of OCT image analysis and measurement on various devices.
A novel signal normalization method for SD-OCT images was developed and tested to improve the direct comparability of image analysis and clinical measurements between devices. The A-scan profile differences between 2 SD-OCT devices were reduced significantly after normalization.
optical coherence tomography; image analysis; comparability
We demonstrate an automated segmentation method for in-vivo 3D optical coherence tomography (OCT) imaging of the lamina cribrosa (LC). Manual segmentations of coronal slices of the LC were used as a gold standard in parameter selection and evaluation of the automated technique. The method was validated using two prototype OCT devices; each had a subject cohort including both healthy and glaucomatous eyes. Automated segmentation of in-vivo 3D LC OCT microstructure performed comparably to manual segmentation and is useful for investigative research and in clinical quantification of the LC.
(100.2000) Digital image processing; (170.4470) Ophthalmology; (110.4500) Optical coherence tomography; (170.1610) Clinical applications; (330.4460) Ophthalmic optics and devices
An upgraded optical coherence tomography system with integrated retinal tracker (TOCT) was developed. The upgraded system uses improved components to extend the tracking bandwidth, fully integrates the tracking hardware into the optical head of the clinical OCT system, and operates from a single software platform. The system was able to achieve transverse scan registration with sub-pixel accuracy (~10 μm). We demonstrate several advanced scan sequences with the TOCT, including composite scans averaged (co-added) from multiple B-scans taken consecutively and several hours apart, en face images collected by summing the A-scans of circular, line, and raster scans, and three-dimensional (3D) retinal maps of the fovea and optic disc. The new system achieves highly accurate OCT scan registration yielding composite images with significantly improved spatial resolution, increased signal-to-noise ratio, and reduced speckle while maintaining well-defined boundaries and sharp fine structure compared to single scans. Precise re-registration of multiple scans over separate imaging sessions demonstrates TOCT utility for longitudinal studies. En face images and 3D data cubes generated from these data reveal high fidelity image registration with tracking, despite scan durations of more than one minute.
BACKGROUND AND OBJECTIVE
To evaluate the ability of structural assessment to predict glaucomatous visual field progression.
PATIENTS AND METHODS
A total of 119 healthy eyes with suspected glaucoma and glaucomatous eyes with 5 or more optic nerve stereophotographs, optical coherence tomography (OCT), and confocal scanning laser ophthalmoscopy (CSLO) all acquired within 6 months of each other were enrolled. Odds ratios to predict progression were determined by generalized estimating equation models.
Median follow-up was 4.0 years (range: 1.5 to 5.7 years). Fifteen eyes progressed by glaucoma progression analysis, 20 by visual field index, and 10 by both. Baseline parameters from stereophotographs (vertical cup-to-disc ratio and Disc Damage Likelihood Scale), OCT (global, superior quadrant, and inferior quadrant retinal nerve fiber layer thickness), and CSLO (cup shape measure and mean cup depth) were significant predictors of progression. Comparing the single best parameter from all models, only the OCT superior quadrant RNFL predicted progression.
Baseline stereophotographs, OCT, and CSLO measurements may be clinically useful to predict glaucomatous visual field progression.
The purpose of this study was to visualize the aqueous outflow system in three dimensions (3D) in living human eyes, and to investigate the use of commercially available Spectral-domain optical coherence tomographic (SD-OCT) systems for this purpose.
This was a prospective observational study.
Participants and/or Controls
One randomly determined eye in each of six normal healthy subjects was included.
3D SD-OCT imaging of the aqueous humor outflow structures was performed with two devices: Cirrus HD-OCT (Carl Zeiss Meditec, Inc., Dublin CA) and Bioptigen SDOIS (Bioptigen, Inc., Research Triangle, NC).
Main Outcome Measures
3D virtual castings of Schlemm’s canal (SC) and more distal outflow structures created from scan data from each device.
Virtual casting of SC provided visualization of more aqueous vessels branching from SC than could be located by interrogating the 2D image stack. Similarly, virtual casting of distal structures allowed visualization of large and small aqueous outflow channel networks that could not be appreciated with the conventional 2D visualization.
The outflow pathways from SC to the superficial vasculature can be identified and tracked in living human eyes using commercially available SD-OCT.
To evaluate the glaucoma discriminating ability of macular retinal layers as measured by spectral-domain optical coherence tomography (SD-OCT).
Healthy, glaucoma suspect and glaucomatous subjects had a comprehensive ocular examination, visual field testing and SD-OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec, Dublin, CA) in the macular and optic nerve head regions. OCT macular scans were segmented into macular nerve fiber layer (mNFL), ganglion cell layer with inner plexiform layer (GCIP), ganglion cell complex (GCC) (composed of mNFL and GCIP), outer retinal complex (ORC) and total retina (TR). Glaucoma discriminating ability was assessed using the area under the receiver operator characteristic curve (AUC) for all macular parameters and mean circumpapillary (cp) RNFL. Glaucoma suspects and glaucoma subjects were grouped together for the calculation of AUCs.
Analysis was performed on 51 healthy, 49 glaucoma suspect and 63 glaucomatous eyes. The median visual field MD was −2.21dB (interquartile range (IQR): −6.92 to −0.35) for the glaucoma group, −0.32dB (IQR: −1.22 to 0.73) for the suspect group and −0.18dB (IQR: −0.92 to 0.71) for the healthy group. Highest age adjusted AUCs for discriminating between healthy and glaucomatous eyes were found for average GCC and GCIP (AUC=0.901 and 0.900, respectively), and their sectoral measurements: infero-temporal (0.922 and 0.913), inferior (0.904 and 0.912) and supero-temporal (0.910 and 0.897). These values were similar to the discriminating ability of the mean cpRNFL (AUC=0.913). Comparison of these AUCs did not yield any statistically significant difference (all p>0.05). Similar discrimination performance but with slight reduction in AUCs was achieved for comparison between healthy and the combination of glaucoma and glaucoma suspect eyes.
SD-OCT GCIP and GCC measurements showed similar glaucoma diagnostic ability and was comparable with that of cpRNFL.
Ocular imaging devices provide quantitative structural information that might improve glaucoma progression detection. This study examined scanning laser polarimetry (SLP) population-derived versus individual-derived cut-off criteria for detecting progression.
Forty-eight healthy, glaucoma suspect and glaucoma subjects, providing 76 eyes were used. All subjects had reliable visual field (VF) and SLP scans acquired at the same visits from ≥ 4 visits. VF progression was defined by guided progression analysis (GPA) and by the VF index (VFI). SLP measurements were analyzed by fast mode (FM) GPA, compared to the population rate of progression, and extended mode (EM) GPA, compared to the individual variability. The agreement between progression detection methods was measured.
Poor agreement was observed between progression defined by VF and FM and EM. The difference in TSNIT average rate of change between VF defined progressors and non-progressors for both FM (p=0.010) and EM (p=0.015) was statistically significant.
There is poor agreement between VF and SLP progression regardless of the use of population derived or individual variability criteria. The best SLP progression detection method could not be ascertained, therefore, acquiring three SLP scans per visit is recommended.
Scanning laser polarimetry; glaucoma progression
We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.
Optical coherence tomography (OCT) is an interferometry-based imaging modality that generates high-resolution cross-sectional images of the retina. Circumpapillary retinal nerve fiber layer (cpRNFL) and optic nerve head assessments are the mainstay of glaucomatous structural measurements in OCT. However, because these measurements are not always available or precise, it would be useful to have another reliable indicator. The macula has been suggested as an alternative scanning location for glaucoma diagnosis. Using time-domain (TD-) OCT, macular measurements have shown to provide good glaucoma diagnostic capabilities. With the adoption of spectral-domain OCT, which allows a higher image resolution than TD-OCT, segmentation of inner macular layers becomes possible. These layers are specifically prone to glaucomatous damage and thickness measurements show a comparable performance to that of glaucomatous cpRNFL measurements. The role of macular measurements for detection of glaucoma progression is still under investigation. More sophisticated measurement and analysis tools that can amplify the advantages of macular measurements are expected. For example, improvement of image quality would allow better visualization, development of various scanning modes would optimize macular measurements, and further refining of the analytical algorithm would provide more accurate segmentation. With these achievements, macular measurement can be an important surrogate for glaucomatous structural assessment.
To evaluate the utility of gold nanorods (AuNRs) as a contrast agent for ocular optical coherence tomography (OCT).
Mice were intravitreally injected with sterile AuNRs coated with either poly(strenesulfate) (PSS-AuNRs) or anti-CD90.2 antibodies (Ab-AuNRs), and imaged using OCT. After 24 hours, eyes were processed for transmission electron microscopy or rendered into single cell suspensions for flow cytometric analysis to determine absolute numbers of CD45+ leukocytes and subsets (T cells, myeloid cells, macrophages, neutrophils). Generalized estimation equations were used to compare cell counts between groups.
PSS-AuNRs and Ab-AuNRs were visualized in the vitreous 30min and 24h post- injection with OCT. At 24h, a statistically significant increase in leukocytes, comprised primarily of neutrophils, was observed in eyes that received either AuNR in comparison to eyes that received saline. The accumulation of leukocytes was equal in eyes given PSS-AuNR or Ab- AuNR. Endotoxin-resistant C3H/HeJ mice also showed ocular inflammation after injection with AuNRs, indicating that the inflammatory response was not due to lipopolysaccharide contamination of AuNRs.
Although AuNRs can be visualized in the eye using OCT they can induce ocular inflammation, which limits their use as a contrast agent.
gold nanoparticles; optical coherence tomography; contrast enhancement; immune response
Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.
Eye movement correction; particle filtering; retinal image processing; three-dimensional optical coherence tomography (3-D OCT)
To develop and test a novel signal enhancement method for optical coherence tomography (OCT) images based on the high dynamic range (HDR) imaging concept.
Three virtual channels, which represent low, medium, and high signal components, were produced for each OCT signal dataset. The dynamic range of each signal component was normalized to the full gray scale range. Finally, the three components were recombined into one image using various weights. Fourteen eyes of 14 healthy volunteers were scanned multiple times using time-domain (TD)-OCT before and while preventing blinking in order to produce a wide variety of signal strength (SS) images on the same eye scanned on the same day. For each eye, a pair of scans with the highest and lowest SS with successful retinal nerve fiber layer (RNFL) segmentation was selected to test the signal enhancement effect. In addition, spectral-domain (SD)-OCT images with poor signal qualities were also processed.
Mean SS of good and poor quality scans were 9.0 ± 1.1 and 4.4 ± 0.9, respectively. TD-OCT RNFL thickness showed significant differences between good and poor quality scans on the same eye (mean difference 11.9 ± 6.0 μm, P < 0.0001, paired t-test), while there was no significant difference after signal enhancement (1.7 ± 6.2 μm, P = 0.33). However, HDR had weaker RNFL compensation effect on images with SS less than or equal to 4, while it maintained good compensation effect on images with SS greater than 4. Successful signal enhancement was also confirmed subjectively on SD-OCT images.
The HDR imaging successfully restored OCT signal and image quality and reduced RNFL thickness differences due to variable signal level to the level within the expected measurement variability. This technique can be applied to both TD- and SD-OCT images.
A novel signal enhancement method for optical coherence tomography images was developed based on the high dynamic range imaging concept, which minimized the retinal nerve fiber layer thickness differences due to various signal levels.
Optical coherence tomography (OCT) captures a major role in clinical assessment in eye care. Innovative hardware and software improvements in the technology would further enhance its usefulness. In this review we present several promising initiatives currently in development or early phase of assessment that we expect to have a future impact on OCT.
optical coherence tomography; OCT; image processing
The broadening frontier of technology used in ocular imaging is continuously affecting the landscape of clinical eye care. With each wave of enhanced imaging modalities, the field faces the difficulties of optimally incorporating these devices into the clinic. Ocular imaging devices have been widely incorporated into clinical management after their diagnostic capabilities have been documented in a wide range of ocular disease. In this review we are presenting the main commercially available devices for imaging of the posterior segment of the eye.
ocular imaging devices; optical coherence tomography; scanning laser ophthalmoscopy; scanning laser polarimetry
Commercial optical coherence tomography (OCT) systems use global signal quality indices to quantify scan quality. Signal quality can vary throughout a scan, contributing to local retinal nerve fibre layer segmentation errors (SegE). The purpose of this study was to develop an automated method, using local scan quality, to predict SegE.
Good-quality (global signal strength (SS)≥6; manufacturer specification) peripapillary circular OCT scans (fast retinal nerve fibre layer scan protocol; Stratus OCT; Carl Zeiss Meditec, Dublin, California, USA) were obtained from 6 healthy, 19 glaucoma-suspect and 43 glaucoma subjects. Scans were grouped based on SegE. Quality index (QI) values were computed for each A-scan using software of our own design. Logistic mixed-effects regression modelling was applied to evaluate SS, global mean and SD of QI, and the probability of SegE.
The difference between local mean QI in SegE regions and No-SegE regions was −5.06 (95% CI −6.38 to 3.734) (p<0.001). Using global mean QI, QI SD and their interaction term resulted in the model of best fit (Akaike information criterion=191.8) for predicting SegE. Global mean QI≥20 or SS≥8 shows little chance for SegE. Once mean QI<20 or SS<8, the probability of SegE increases as QI SD increases.
When combined with a signal quality parameter, the variation of signal quality between A-scans provides significant information about the quality of an OCT scan and can be used as a predictor of segmentation error.
To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes.
192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements.
The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes.
A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage.
To describe morphometric details of the human aqueous humor (AH) outflow microvasculature visualized with 360-degree virtual castings during active AH outflow in cadaver eyes and to compare these structures with corrosion casting studies.
The conventional AH outflow pathways of donor eyes (n = 7) and eyes in vivo (n = 3) were imaged with spectral-domain optical coherence tomography (SD-OCT) and wide-bandwidth superluminescent diode array during active AH outflow. Digital image contrast was adjusted to isolate AH microvasculature, and images were viewed in a 3D viewer. Additional eyes (n = 3) were perfused with mock AH containing fluorescent tracer microspheres to compare microvasculature patterns.
Observations revealed components of the conventional outflow pathway from Schlemm's canal (SC) to the superficial intrascleral venous plexus (ISVP). The superficial ISVP in both our study and corrosion casts were composed of interconnected venules (10–50 μm) forming a hexagonal meshwork. Larger radial arcades (50–100 μm) drained the region nearest SC and converged with larger tortuous vessels (>100 μm). A 360-degree virtual casting closely approximated corrosion casting studies. Tracer studies corroborated our findings. Tracer decorated several larger vessels (50–100 μm) extending posteriorly from the limbus in both raw and contrast-enhanced fluorescence images. Smaller tracer-labeled vessels (30–40 μm) were seen branching between larger vessels and exhibited a similar hexagonal network pattern.
SD-OCT is capable of detailed morphometric analysis of the conventional outflow pathway in vivo or ex vivo with details comparable to corrosion casting techniques.
A three-dimensional morphometric virtual casting of the conventional aqueous outflow microvasculature was created from spectral-domain optical coherence tomography, and compared to fluorescent tracer microsphere and corrosion casting imagery.
To investigate the effects of central corneal thickness (CCT)-associated variants on primary open-angle glaucoma (POAG) risk using single nucleotide polymorphisms (SNP) data from the Glaucoma Genes and Environment (GLAUGEN) and National Eye Institute (NEI) Glaucoma Human Genetics Collaboration (NEIGHBOR) consortia.
A replication analysis of previously reported CCT SNPs was performed in a CCT dataset (n = 1117) and these SNPs were then tested for association with POAG using a larger POAG dataset (n = 6470). Then a CCT genome-wide association study (GWAS) was performed. Top SNPs from this analysis were selected and tested for association with POAG. cDNA libraries from fetal and adult brain and ocular tissue samples were generated and used for candidate gene expression analysis.
Association with one of 20 previously published CCT SNPs was replicated: rs12447690, near the ZNF469 gene (P = 0.001; β = −5.08 μm/allele). None of these SNPs were significantly associated with POAG. In the CCT GWAS, no SNPs reached genome-wide significance. After testing 50 candidate SNPs for association with POAG, one SNP was identified, rs7481514 within the neurotrimin (NTM) gene, that was significantly associated with POAG in a low-tension subset (P = 0.00099; Odds Ratio [OR] = 1.28). Additionally, SNPs in the CNTNAP4 gene showed suggestive association with POAG (top SNP = rs1428758; P = 0.018; OR = 0.84). NTM and CNTNAP4 were shown to be expressed in ocular tissues.
The results suggest previously reported CCT loci are not significantly associated with POAG susceptibility. By performing a quantitative analysis of CCT and a subsequent analysis of POAG, SNPs in two cell adhesion molecules, NTM and CNTNAP4, were identified and may increase POAG susceptibility in a subset of cases.
Reduced central corneal thickness (CCT) is associated with primary open-angle glaucoma (POAG) risk. We investigated the effects of known CCT-associated variants, performed a genome-wide analysis of CCT, and tested the effects of these SNPs on POAG.
compared retinal nerve fiber layer (RNFL) bias and imprecision among three spectral-domain optical coherence tomographs (SD-OCT).
A total of 152 eyes of 83 subjects (96 healthy and 56 glaucomatous eyes) underwent peripapillary RNFL imaging using at least 2 of the following 3 SD-OCT devices on the same day: Cirrus HD-OCT (optic nerve head [ONH]) cube 200 × 200 protocol), RTVue-100 (ONH protocol [12 radial lines and 13 concentric circles]), and 3D OCT-1000 (3D Scan 256 × 256 protocol). Calibration equations, bias and imprecision of RNFL measurements were calculated using structural equation models.
The calibration equations for healthy and glaucoma RNFL thickness measurements among the 3 devices were: Cirrus = 2.136 + 0.831*RTVue; Cirrus = −15.521 + 1.056*3D OCT-1000; RTVue = −21.257 + 1.271*3D OCT-1000. Using Cirrus bias as an arbitrary reference, RTVue bias was 1.20 (95% CI 1.09–1.32, P < 0.05) times larger and 3D OCT-1000 was 0.95 (0.87–1.03, P > 0.05) times smaller. Relative to 3D OCT-1000, the RTVue bias was 1.27 (1.13–1.42, P < 0.05). RTVue imprecision (healthy eyes 7.83, 95% CI 6.43–9.58; glaucoma cases 5.71, 4.19–7.64) was statistically significantly higher than both Cirrus (healthy eyes 3.23, 2.11–4.31; glaucoma cases 3.53, 0.69–5.24) and 3D OCT-1000 (healthy eyes 4.07, 3.11–5.35; glaucoma cases 5.33, 3.77–7.67) in healthy eyes. The imprecision also was significantly higher for RTVue measurements in healthy compared to glaucomatous eyes. None of the other comparisons was statistically significant.
RTVue-100 showed higher imprecision (or higher measurement variability) than Cirrus HD-OCT and 3D OCT-1000 RNFL measurements. Three-dimensional cube scanning with post-hoc data sampling may be a factor reducing imprecision.
RTVue-100 showed worse imprecision (higher measurement variability) than Cirrus HD-OCT and 3D OCT-1000 retinal nerve fiber layer measurements. The results might be related to the use of raster scanning by the later devices, while RTVue uses a combination of radial and concentric scanning pattern.
Understanding the effects of IOP on the optic nerve head (ONH) is important in understanding glaucoma and ONH structure and function. The authors tested the hypothesis that the ONH is a robust biomechanical structure wherein various factors combine to produce a relatively stable response to IOP.
The authors generated two populations of 100,000 ONH numerical models each with randomly selected values, but controlled distributions, either uniform or Gaussian, of ONH geometry and mechanical properties. The authors predicted the lamina cribrosa displacement (LCD), scleral canal expansion (SCE), and the stresses (forces) and deformations (strains) produced by a 10 mm Hg increase in IOP. The authors analyzed the distributions of the responses.
The responses were distributed nonuniformly, with the majority of the models having a response within a small region, often less than 30% of the size of the overall response region. This concentration of responses was more marked in the Gaussian population than in the uniform population. All the responses were positively skewed. Whether a particular case was typical or not depended on the response used for classification and on whether the decision was made using one-dimensional or two-dimensional criteria.
Despite wide variations in ONH characteristics and responses to IOP, some responses were much more common than others. This supports conceiving of the eye as a robust structure, particularly for LCD and SCE, which is tolerant to variations in tissue geometry and mechanical properties. The authors also provide the first estimates of the typical mechanical response of the ONH to variations in IOP over a large population of ONHs.
The authors evaluated a large population of models and found that despite wide variations in ONH characteristics and responses to IOP some mechanical effects of IOP were more common than others. This supports conceiving of the eye as a robust biomechanical system which is tolerant to variations in tissue geometry and properties.
We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC > 0.93).
computer-aided diagnosis (CAD); optical coherence tomography (OCT); macular pathology; multi-scale spatial pyramid (MSSP); local binary patterns (LBP); principle component analysis (PCA); Support Vector Machine (SVM)
Three-dimensional optical coherence tomography (OCT) is a new ophthalmic imaging technique offering more detailed quantitative analysis of the retinal structure. Eye movement during 3D OCT scanning, however, creates significant spatial distortions that may adversely affect image interpretation and analysis. Current software solutions must use additional reference images or B-scans to correct eye movement in a certain direction. The proposed particle filtering algorithm is an independent 3D alignment approach, which does not rely on any reference image. 3D OCT data is considered as a dynamic system, while location of A-scan is represented by the state space. A particle set is generated to approximate the probability density of the state. The state of the system is updated frame by frame to detect A-scan movement. Seventy-four 3D OCT images with eye movement were tested and subjectively evaluated by comparing them with the original images. All the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3D OCT volume data and correct the eye movement without using references.
Particle Filtering; 3D OCT; Eye Movement Correction; Retinal Image Processing
We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local descriptors are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture information from OCT images of the retina. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. We conducted extensive experiments on a large dataset consisting of 326 OCT scans from 136 patients. The results show that the proposed method is very effective.
Aqueous humor (AH) exiting the eye via the trabecular meshwork and Schlemm's canal (SC) passes through the deep and intrascleral venous plexus (ISVP) or directly through aqueous veins. The purpose of this study was to visualize the human AH outflow system 360 degrees in three dimensions (3D) during active AH outflow in a virtual casting.
The conventional A Houtflow pathways of 7 donor eyes were imaged with a modified Bioptigen spectral-domain optical coherence tomography system (Bioptigen Inc, USA; SuperLum LTD, Ireland) at a perfusion pressure of 20 mmHg (N=3), and 10 mmHg (N=4). In all eyes, 36 scans (3 equally distributed in each clock hour), each covering a 2 × 3 × 2 mm volume (512 frames, each 512 × 1024 pixels), were obtained. All image data were black/white inverted, and the background subtracted (ImageJ 1.40g, http://rsb.info.nih.gov/ij/). Contrast was adjusted to isolate the ISVP.
SC, collector channels, the deep and ISVP, and episcleral veins were observed throughout the limbus. Aqueous veins could be observed extending into the episcleral veins. Individual scan ISVP castings were rendered and assembled in 3D space in Amira 4.1 (Visage Imaging Inc. USA). A 360-degree casting of the ISVP was obtained in all perfused eyes. The ISVP tended to be dense and overlapping in the superior and inferior quadrants, and thinner in the lateral quadrants.
The human AH outflow pathway can be imaged using SD-OCT. The more superficial structures of the AH outflow pathway present with sufficient contrast as to be optically isolated and cast in-situ 360 degrees in cadaver eye perfusion models. This approach may be useful as a model in future studies of human AH outflow.