Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images

3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images

Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD) by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1) preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS) junction layer and Bruch’s membrane (BM) retinal layers; (2) coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3) fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF) for the segmentation of drusenoid regions were found to be 89.15% ± 3.76 and 0.17% ± .18%, respectively.

Enhanced Visualization of Subtle Outer Retinal Pathology by En Face Optical Coherence Tomography and Correlation with Multi-Modal Imaging

PURPOSE

To present en face optical coherence tomography (OCT) images generated by graph-search theory algorithm-based custom software and examine correlation with other imaging modalities.

METHODS

En face OCT images derived from high density OCT volumetric scans of 3 healthy subjects and 4 patients using a custom algorithm (graph-search theory) and commercial software (Heidelberg Eye Explorer software (Heidelberg Engineering)) were compared and correlated with near infrared reflectance, fundus autofluorescence, adaptive optics flood-illumination ophthalmoscopy (AO-FIO) and microperimetry.

RESULTS

Commercial software was unable to generate accurate en face OCT images in eyes with retinal pigment epithelium (RPE) pathology due to segmentation error at the level of Bruch's membrane (BM). Accurate segmentation of the basal RPE and BM was achieved using custom software. The en face OCT images from eyes with isolated interdigitation or ellipsoid zone pathology were of similar quality between custom software and Heidelberg Eye Explorer software in the absence of any other significant outer retinal pathology. En face OCT images demonstrated angioid streaks, lesions of acute macular neuroretinopathy, hydroxychloroquine toxicity and Bietti crystalline deposits that correlated with other imaging modalities.

CONCLUSIONS

Graph-search theory algorithm helps to overcome the limitations of outer retinal segmentation inaccuracies in commercial software. En face OCT images can provide detailed topography of the reflectivity within a specific layer of the retina which correlates with other forms of fundus imaging. Our results highlight the need for standardization of image reflectivity to facilitate quantification of en face OCT images and longitudinal analysis.

Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes

Patient motion artifacts are often visible in densely sampled or large wide field-of-view (FOV) retinal optical coherence tomography (OCT) volumes. A popular strategy for reducing motion artifacts is to capture two orthogonally oriented volumetric scans. However, due to larger volume sizes, longer acquisition times, and corresponding larger motion artifacts, the registration of wide FOV scans remains a challenging problem. In particular, gaps in data acquisition due to eye motion, such as saccades, can be significant and their modeling becomes critical for successful registration. In this article, we develop a complete computational pipeline for the automatic motion correction and accurate registration of wide FOV orthogonally scanned OCT images of the human retina. The proposed framework utilizes the retinal boundary segmentation as a guide for registration and requires only a minimal transformation of the acquired data to produce a successful registration. It includes saccade detection and correction, a custom version of the optical flow algorithm for dense lateral registration and a linear optimization approach for axial registration. Utilizing a wide FOV swept source OCT system, we acquired retinal volumes of 12 subjects and we provide qualitative and quantitative experimental results to validate the state-of-the-art effectiveness of the proposed technique. The source code corresponding to the proposed algorithm is available online.

Automated choroid segmentation in three-dimensional 1-μm wide-view OCT images with gradient and regional costs

Choroid thickness and volume estimated from optical coherence tomography (OCT) images have emerged as important metrics in disease management. This paper presents an automated three-dimensional (3-D) method for segmenting the choroid from 1 - ? m wide-view swept source OCT image volumes, including the Bruch’s membrane (BM) and the choroidal–scleral interface (CSI) segmentation. Two auxiliary boundaries are first detected by modified Canny operators and then the optical nerve head is detected and removed. The BM and the initial CSI segmentation are achieved by 3-D multiresolution graph search with gradient-based cost. The CSI is further refined by adding a regional cost, calculated from the wavelet-based gradual intensity distance. The segmentation accuracy is quantitatively evaluated on 32 normal eyes by comparing with manual segmentation and by reproducibility test. The mean choroid thickness difference from the manual segmentation is 19.16 ± 4.32 ?? ? m , the mean Dice similarity coefficient is 93.17 ± 1.30 % , and the correlation coefficients between fovea-centered volumes obtained on repeated scans are larger than 0.97.

Multimodal registration of SD-OCT volumes and fundus photographs using histograms of oriented gradients

With availability of different retinal imaging modalities such as fundus photography and spectral domain optical coherence tomography (SD-OCT), having a robust and accurate registration scheme to enable utilization of this complementary information is beneficial. The few existing fundus-OCT registration approaches contain a vessel segmentation step, as the retinal blood vessels are the most dominant structures that are in common between the pair of images. However, errors in the vessel segmentation from either modality may cause corresponding errors in the registration. In this paper, we propose a feature-based registration method for registering fundus photographs and SD-OCT projection images that benefits from vasculature structural information without requiring blood vessel segmentation. In particular, after a preprocessing step, a set of control points (CPs) are identified by looking for the corners in the images. Next, each CP is represented by a feature vector which encodes the local structural information via computing the histograms of oriented gradients (HOG) from the neighborhood of each CP. The best matching CPs are identified by calculating the distance of their corresponding feature vectors. After removing the incorrect matches the best affine transform that registers fundus photographs to SD-OCT projection images is computed using the random sample consensus (RANSAC) method. The proposed method was tested on 44 pairs of fundus and SD-OCT projection images of glaucoma patients and the result showed that the proposed method successfully registers the multimodal images and produced a registration error of 25.34 ± 12.34 μm (0.84 ± 0.41 pixels).

Influence of coherence length, signal-to-noise ratio, log transform, and low-pass filtering on layer thickness assessment with OCT in the retina

Optical coherence tomography (OCT) images of the retina are inevitably affected by the finite width of the coherence function and noise. To make low-reflective layers visible, the raw OCT signal is log transformed; to reduce the effect of noise the images can be low-pass filtered. We determined the effects of these operations on layer thickness assessment, as a function of signal-to-noise ratio (SNR), by performing measurements in a phantom eye and modeling. The log transform appeared to be the key factor in a SNR-dependent overestimation of peak widths and a less predictive bias in the widths of low-reflective layers.

Automated classification of optical coherence tomography images of human atrial tissue

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

GANGLION CELL LAYER THICKNESS AND VISUAL IMPROVEMENT AFTER EPIRETINAL MEMBRANE SURGERY

PURPOSE

To evaluate the possible prognostic value of preoperative individual retinal layer thicknesses measured by an automated algorithm from spectral domain optical coherence tomography and visual acuity or improvement after epiretinal membrane surgery.

METHODS

Data from 76 eyes with idiopathic epiretinal membrane that underwent pars plana vitrectomy for idiopathic epiretinal membrane removal were analyzed. The preoperative thicknesses of the ganglion cell layer, inner plexiform layer, and other layers were measured using the Iowa Reference Algorithm. Each retinal layer thickness and its ratio of the central foveal thickness were compared between eyes with (Group 1) or without (Group 2) 2 or more Snellen lines of visual improvement at 3, 6, and 12 months after surgery.

RESULTS

Higher mean central foveal thickness/ganglion cell layer ratio and symptom duration of ≤1 year were significantly more common in Group 1 (P = 0.03 and 0.04, respectively). After adjusting for age and symptom duration, lens status, and preoperative visual acuity, higher central foveal thickness/ganglion cell layer ratio was associated with ≥2 lines of visual improvement after surgery (odds ratio: 6.57, 95% confidence interval: 1.29-33.40).

CONCLUSION

The preoperative inner retinal layer changes may have a role independent of outer retinal layer parameters in the visual prognosis after epiretinal membrane peeling.

Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices

Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and agreement between OCT systems and segmentation methods was assessed. Inter-session coefficient of repeatability (CoR) for the foveal subfield total retinal thickness was 3.43μm, 4.76μm, and 5.98μm for the Zeiss, Topcon, and long-wavelength images respectively. For the commercial software, CoR was 4.63μm (Zeiss) and 7.63μm (Topcon). The Iowa Reference Algorithms demonstrated higher repeatability than the on-board software and, in addition, reliably segmented all 10 intra-retinal layers. With fixed-AEL scaling, the algorithm produced significantly different thickness values for the three OCT devices (P<0.05), with these discrepancies generally characterized by an overall offset (bias) and correlations with axial eye length for the foveal subfield and outer ring (P<0.05). This correlation was reduced to an insignificant level in all cases when AEL-dependent scaling was used. Overall, the Iowa Reference Algorithms are viable for clinical and research use in healthy eyes imaged with these devices, however ocular biometry is required for accurate quantification of OCT images.

Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease

In macular spectral domain optical coherence tomography (SD-OCT) volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3 µm in cystoid macular edema and 262 µm in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in "big data." Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

3D reconstruction of cochlea using optical coherence tomography

Recently, in vivo visualization of the cochlea and the smaller structures inside of it has been achieved by optical coherence tomography (OCT). This makes it possible to use OCT imaging for diagnosis of diseases such as Meniere's disease through measuring the degree of endolymphatic hydrops. To this end, we present a novel method for 3D segmentation of these cochlear OCT images that is based on superpixels and diffusion maps. The method takes as input grayscale volumetric OCT images and outputs a binary image with the segmented cochlea. We show that the proposed method is suitable for segmenting the data for visualization as well as for preprocessing the data for future automated grading of endolymphatic hydrops.

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.

Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach

Using the graph-based a simple linear iterative clustering (SLIC) superpixels and manifold ranking technology, a novel automated intra-retinal layer segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel in most existing segmentation methods, the proposed method focuses on the superpixels and the connected components-based image cues. The image is represented as some weighted graphs with superpixels or connected components as nodes. Each node is ranked with the gradient and spatial distance cues via graph-based Dijkstra's method or manifold ranking. So that it can effectively overcome speckle noise, organic texture and blood vessel artifacts issues. Segmentation is carried out in a three-stage scheme to extract eleven boundaries efficiently. The segmentation algorithm is validated on 2D and 3D OCT images in three databases, and is compared with the manual tracings of two independent observers. It demonstrates promising results in term of the mean unsigned boundaries errors, the mean signed boundaries errors, and layers thickness errors.

Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes

The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes. In particular, the gradient vector flow (GVF) field, which is computed from a multiresolution initial segmentation, is employed for calculating a set of non-overlapping GVF-based columns perpendicular to the initial segmentation. The GVF columns are utilized to resample the volume and also serve as the columns to the graph construction. The ILM surface in the resampled volume is fairly smooth and does not contain the steep slopes. This prior shape knowledge along with the blood vessel information, obtained from registered fundus photographs, are incorporated in a graph-theoretic approach in order to identify the location of the ILM surface. The proposed method is tested on the SD-OCT volumes of 44 subjects with various stages of glaucoma and significantly smaller segmentation errors were obtained than that of current approaches.

A Review of Adaptive Optics Optical Coherence Tomography: Technical Advances, Scientific Applications, and the Future

PURPOSE

Optical coherence tomography (OCT) has enabled "virtual biopsy" of the living human retina, revolutionizing both basic retina research and clinical practice over the past 25 years. For most of those years, in parallel, adaptive optics (AO) has been used to improve the transverse resolution of ophthalmoscopes to foster in vivo study of the retina at the microscopic level. Here, we review work done over the last 15 years to combine the microscopic transverse resolution of AO with the microscopic axial resolution of OCT, building AO-OCT systems with the highest three-dimensional resolution of any existing retinal imaging modality.

METHODS

We surveyed the literature to identify the most influential antecedent work, important milestones in the development of AO-OCT technology, its applications that have yielded new knowledge, research areas into which it may productively expand, and nascent applications that have the potential to grow.

RESULTS

Initial efforts focused on demonstrating three-dimensional resolution. Since then, many improvements have been made in resolution and speed, as well as other enhancements of acquisition and postprocessing techniques. Progress on these fronts has produced numerous discoveries about the anatomy, function, and optical properties of the retina.

CONCLUSIONS

Adaptive optics OCT continues to evolve technically and to contribute to our basic and clinical knowledge of the retina. Due to its capacity to reveal cellular and microscopic detail invisible to clinical OCT systems, it is an ideal companion to those instruments and has the demonstrable potential to produce images that can guide the interpretation of clinical findings.

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.

Performance evaluation of automated segmentation software on optical coherence tomography volume data

Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research-oriented tools for automated segmentation of the retinal tissue on OCT images. It also evaluates and compares the performance of these software tools with a common ground truth.

Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography

This paper describes a complex correlation mapping algorithm for optical coherence angiography (cmOCA). The proposed algorithm avoids the signal-to-noise ratio dependence and exhibits low noise in vasculature imaging. The complex correlation coefficient of the signals, rather than that of the measured data are estimated, and two-step averaging is introduced. Algorithms of motion artifact removal based on non perfusing tissue detection using correlation are developed. The algorithms are implemented with Jones-matrix OCT. Simultaneous imaging of pigmented tissue and vasculature is also achieved using degree of polarization uniformity imaging with cmOCA. An application of cmOCA to in vivo posterior human eyes is presented to demonstrate that high-contrast images of patients' eyes can be obtained.

Voxel Based Morphometry in Optical Coherence Tomography: Validation & Core Findings

Optical coherence tomography (OCT) of the human retina is now becoming established as an important modality for the detection and tracking of various ocular diseases. Voxel based morphometry (VBM) is a long standing neuroimaging analysis technique that allows for the exploration of the regional differences in the brain. There has been limited work done in developing registration based methods for OCT, which has hampered the advancement of VBM analyses in OCT based population studies. Following on from our recent development of an OCT registration method, we explore the potential benefits of VBM analysis in cohorts of healthy controls (HCs) and multiple sclerosis (MS) patients. Specifically, we validate the stability of VBM analysis in two pools of HCs showing no significant difference between the two populations. Additionally, we also present a retrospective study of age and sex matched HCs and relapsing remitting MS patients, demonstrating results consistent with the reported literature while providing insight into the retinal changes associated with this MS subtype.

Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes

Optical coherence tomography (OCT) is a noninvasive imaging modality that has begun to find widespread use in retinal imaging for the detection of a variety of ocular diseases. In addition to structural changes in the form of altered retinal layer thicknesses, pathological conditions may also cause the formation of edema within the retina. In multiple sclerosis, for instance, the nerve fiber and ganglion cell layers are known to thin. Additionally, the formation of pseudocysts called microcystic macular edema (MME) have also been observed in the eyes of about 5% of MS patients, and its presence has been shown to be correlated with disease severity. Previously, we proposed separate algorithms for the segmentation of retinal layers and MME, but since MME mainly occurs within specific regions of the retina, a simultaneous approach is advantageous. In this work, we propose an automated globally optimal graph-theoretic approach that simultaneously segments the retinal layers and the MME in volumetric OCT scans. SD-OCT scans from one eye of 12 MS patients with known MME and 8 healthy controls were acquired and the pseudocysts manually traced. The overall precision and recall of the pseudocyst detection was found to be 86.0% and 79.5%, respectively.

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

PURPOSE

To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies.

METHODS

Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm3) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis.

RESULTS

The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01).

CONCLUSIONS

The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected.

TRANSLATIONAL RELEVANCE

Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.

An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.

Enface Thickness Mapping and Reflectance Imaging of Retinal Layers in Diabetic Retinopathy

Purpose

To present a method for image segmentation and generation of enface thickness maps and reflectance images of retinal layers in healthy and diabetic retinopathy (DR) subjects.

Methods

High density spectral domain optical coherence tomography (SDOCT) images were acquired in 10 healthy and 4 DR subjects. Customized image analysis software identified 5 retinal cell layer interfaces and generated thickness maps and reflectance images of the total retina (TR), inner retina (IR), outer retina (OR), and the inner segment ellipsoid (ISe) band. Thickness maps in DR subjects were compared to those of healthy subjects by generating deviation maps which displayed retinal locations with thickness below, within, and above the normal 95% confidence interval.

Results

In healthy subjects, TR and IR thickness maps displayed the foveal depression and increased thickness in the parafoveal region. OR and ISe thickness maps showed increased thickness at the fovea, consistent with normal retinal anatomy. In DR subjects, thickening and thinning in localized regions were demonstrated on TR, IR, OR, and ISe thickness maps, corresponding to retinal edema and atrophy, respectively. TR and OR reflectance images showed reduced reflectivity in regions of increased thickness. Hard exudates appeared as hyper-reflective spots in IR reflectance images and casted shadows on the deeper OR and ISe reflectance images. The ISe reflectance image clearly showed the presence of focal laser scars.

Conclusions

Enface thickness mapping and reflectance imaging of retinal layers is a potentially useful method for quantifying the spatial and axial extent of pathologies due to DR.

Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection

Glaucoma is one of the most common causes of blindness worldwide. Early detection of glaucoma is traditionally based on assessment of the cup-to-disc (C/D) ratio, an important indicator of structural changes to the optic nerve head. Here, we present an automated optic disc segmentation algorithm in 3-D spectral domain optical coherence tomography (SD-OCT) volumes to quantify this ratio. The proposed algorithm utilizes a two-stage strategy. First, it detects the neural canal opening (NCO) by finding the points with maximum curvature on the retinal pigment epithelium (RPE) boundary with a spatial correlation smoothness constraint on consecutive B-scans, and it approximately locates the coarse disc margin in the projection image using convex hull fitting. Then, a patch searching procedure using a probabilistic support vector machine (SVM) classifier finds the most likely patch with the NCO in its center in order to refine the segmentation result. Thus, a reference plane can be determined to calculate the C/D radio. Experimental results on 42 SD-OCT volumes from 17 glaucoma patients demonstrate that the proposed algorithm can achieve high segmentation accuracy and a low C/D ratio evaluation error. The unsigned border error for optic disc segmentation and the evaluation error for C/D ratio comparing with manual segmentation are 2.216 ± 1.406 pixels (0.067 ± 0.042 mm) and 0.045 ± 0.033, respectively.

Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section

A variety of vision ailments are indicated by anomalies in the choroid layer of the posterior visual section. Consequently, choroidal thickness and volume measurements, usually performed by experts based on optical coherence tomography (OCT) images, have assumed diagnostic significance. Now, to save precious expert time, it has become imperative to develop automated methods. To this end, one requires choroid outer boundary (COB) detection as a crucial step, where difficulty arises as the COB divides the choroidal granularity and the scleral uniformity only notionally, without marked brightness variation. In this backdrop, we measure the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimate the COB by thresholding. Subsequently, smooth COB estimates, mimicking manual delineation, are obtained using tensor voting. On five datasets, each consisting of 97 adult OCT B-scans, automated and manual segmentation results agree visually. We also demonstrate close statistical match (greater than 99.6% correlation) between choroidal thickness distributions obtained algorithmically and manually. Further, quantitative superiority of our method is established over existing results by respective factors of 27.67% and 76.04% in two quotient measures defined relative to observer repeatability. Finally, automated choroidal volume estimation, being attempted for the first time, also yields results in close agreement with that of manual methods.

Visual Prognosis of Eyes Recovering From Macular Hole Surgery Through Automated Quantitative Analysis of Spectral-Domain Optical Coherence Tomography (SD-OCT) Scans

PURPOSE

To determine the value of topographic spectral-domain optical coherence tomography (SD-OCT) imaging features assessed after macular hole repair surgery in predicting visual acuity (VA) outcomes.

METHODS

An automated algorithm was developed to topographically outline and quantify area, extent, and location of defects in the ellipsoid zone (EZ) band and inner retina layers in SD-OCT scans. We analyzed the correlation of these values with VA in longitudinal observations from 35 patients who underwent successful macular hole surgery, in their first observation after surgery (within 2 months), and in a single observation within 6 to 12 months after surgery. Image features assessed at the first visit after surgery were also investigated as possible predictors of future VA improvement.

RESULTS

Significant correlation with longitudinal VA was found for the extent, circularity, and ratio of defects in EZ band at the fovea and parafoveal regions. The ratio of defects in EZ band at the fovea, temporal-inner, and inferior-inner macula regions showed significant strong correlation with VA within 6 to 12 months post surgery. Patients with worse vision outcome at such time also had a significantly higher rate of inner retinal defects in the superior-outer region in their first postsurgery observation.

CONCLUSIONS

A lowering extent of EZ band defects in the foveal and parafoveal regions is a good indicator of postsurgery VA recovery. Attention should also be given to postsurgical alterations in the inner retina, as patients with more extensive atrophic changes appear to have slower or worse VA recovery despite closure of the macular hole.

Outer Segment Thickness Predicts Visual Field Response to QLT091001 in Patients with RPE65 or LRAT Mutations

PURPOSE

To determine whether the degree of change in Goldmann visual fields (GVFs) following oral administration of QLT091001 was related to baseline measures of retinal structure.

METHODS

Oral QLT091001 was administered once daily for 7 days in all study patients. Comprehensive ophthalmic testing, including spectral-domain optical coherence tomography (SD-OCT), was conducted in 14 patients with Leber congenital amaurosis (LCA) and 18 patients with retinitis pigmentosa (RP) at seven international sites. Average thickness of the outer segment (OS) layer was calculated over central 20°. Both eyes of each subject were evaluated separately.

RESULTS

Nineteen of 28 eyes (68%) with LCA and 13 of 36 eyes (36%) with RP responded to QLT091001. Among these responders, the average baseline thickness of the OS layer (central 20°) was 13.5 μm in the LCA cohort and 11.7 μm in the RP cohort. Nonresponders had average baseline OS thickness of less than 4.6 μm in both cohorts. The OS thickness in the central 20° was significantly shorter in nonresponders than responders in the LCA cohort (P = 0.01, t-test) and in the RP cohort (P = 0.02, Wilcoxon rank sum test). The OS thickness in the central 20° did not change significantly from baseline during the first 2 months (P = 0.09, t-test, paired).

CONCLUSIONS

The present findings suggest that there is a close parallel between the thickness of the photoreceptor layer and the potential for functional improvement in these patients.

TRANSLATIONAL RELEVANCE

SD-OCT thickness in the central retina may be useful for predicting the visual field response in the peripheral retina to QLT091001. (https://clinicaltrials.gov/ct2/show/NCT01014052 number, NCT01014052).

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

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

PURPOSE

To evaluate the validity of a novel fully automated three-dimensional (3D) method capable of segmenting the choroid from two different optical coherence tomography scanners: swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT).

METHODS

One hundred eight subjects were imaged using SS-OCT and SD-OCT. A 3D method was used to segment the choroid and quantify the choroidal thickness along each A-scan. The segmented choroidal posterior boundary was evaluated by comparing to manual segmentation. Differences were assessed to test the agreement between segmentation results of the same subject. Choroidal thickness was defined as the Euclidian distance between Bruch's membrane and the choroidal posterior boundary, and reproducibility was analyzed using automatically and manually determined choroidal thicknesses.

RESULTS

For SS-OCT, the average choroidal thickness of the entire 6- by 6-mm2 macular region was 219.5 μm (95% confidence interval [CI], 204.9-234.2 μm), and for SD-OCT it was 209.5 μm (95% CI, 197.9-221.0 μm). The agreement between automated and manual segmentations was high: Average relative difference was less than 5 μm, and average absolute difference was less than 15 μm. Reproducibility of choroidal thickness between repeated SS-OCT scans was high (coefficient of variation [CV] of 3.3%, intraclass correlation coefficient [ICC] of 0.98), and differences between SS-OCT and SD-OCT results were small (CV of 11.0%, ICC of 0.73).

CONCLUSIONS

We have developed a fully automated 3D method for segmenting the choroid and quantifying choroidal thickness along each A-scan. The method yielded high validity. Our method can be used reliably to study local choroidal changes and may improve the diagnosis and management of patients with ocular diseases in which the choroid is affected.

Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography

Optical coherence tomography (OCT) yields high-resolution, three-dimensional images of the retina. Reliable segmentation of the retinal layers is necessary for the extraction of clinically useful information. We present a novel segmentation method that operates on attenuation coefficients and incorporates anatomical knowledge about the retina. The attenuation coefficients are derived from in-vivo human retinal OCT data and represent an optical property of the tissue. Then, the layers in the retina are simultaneously segmented via a new flexible coupling approach that exploits the predefined order of the layers. The accuracy of the method was evaluated on 20 peripapillary scans of healthy subjects. Ten of those subjects were imaged again to evaluate the reproducibility. An additional evaluation was performed to examine the robustness of the method on a variety of data: scans of glaucoma patients, macular scans and scans by a two different OCT imaging devices. A very good agreement on all data was found between the manual segmentation performed by a medical doctor and the segmentation obtained by the automatic method. The mean absolute deviation for all interfaces in all data types varied between 1.9 and 8.5 µm (0.5-2.2 pixels). The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation.

Causes and Prognosis of Visual Acuity Loss at the Time of Initial Presentation in Idiopathic Intracranial Hypertension

PURPOSE

To determine the etiology and prognosis of visual acuity loss in idiopathic intracranial hypertension (IIH) at presentation and to provide objective measures to predict visual outcome.

METHODS

A retrospective review of 660 patients with IIH (2009-2013) identified 31 patients (4.7%) with 48 eyes having best-corrected visual acuity (BCVA) of 20/25 or worse on initial presentation. Fundus photography, optical coherence tomography (OCT) of the optic disc and macula, and perimetry were used to determine the causes and prognosis of vision loss. Segmentation of the macula OCT was performed using the Iowa Reference Algorithm to determine the retinal ganglion cell-inner plexiform layer complex (GCL-IPL) thickness.

RESULTS

Outer retinal changes alone caused decreased BCVA at initial presentation in 22 eyes (46%): subretinal fluid in 16, chorioretinal folds in 5, and peripapillary choroidal neovascularization in 1. The vision loss was reversible except for some eyes with chorioretinal folds. Optic neuropathy alone caused decreased BCVA in 10 eyes (21%) and coexisting outer retinal changes and optic neuropathy caused decreased BCVA in 16 eyes (33%). A GCL-IPL thickness less than or equal to 70 μm at initial presentation or progressive thinning of greater than or equal to 10 μm within 2 to 3 weeks compared with baseline correlated with poor visual outcome.

CONCLUSIONS

Visual acuity loss in IIH can be caused by both outer retinal changes and optic neuropathy. Vision loss from outer retinal changes is mostly reversible. The outcome of patients with coexisting outer retinal changes and optic neuropathy or optic neuropathy alone depends on the degree of optic neuropathy, which can be predicted by the GCL-IPL thickness.

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

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

Restricted Summed-Area Projection for Geographic Atrophy Visualization in SD-OCT Images

PURPOSE

To enhance the rapid assessment of geographic atrophy (GA) across the macula in a single projection image generated from three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) scans by introducing a novel restricted summed-area projection (RSAP) technique.

METHODS

We describe a novel en face GA visualization technique, the RSAP, by restricting the axial projection of SD-OCT images to the regions beneath the Bruch's membrane (BM) boundary and also considering the choroidal vasculature's influence on GA visualization. The technique analyzes the intensity distribution beneath the retinal pigment epithelium (RPE) layer to fit a cross-sectional surface in the sub-RPE region. The area is taken as the primary GA projection. A median filter is then adopted to smooth the generated GA projection image. The RSAP technique was evaluated in 99 3D SD-OCT data sets from 27 eyes of 21 patients presenting with advanced nonexudative age-related macular degeneration and GA. We used the mean difference between GA and background regions and GA separability metric to measure GA contrast and distinction in the generated images, respectively. We compared our results with two existing GA projection techniques, the summed-voxel projection (SVP) and Sub-RPE Slab techniques.

RESULTS

Comparative results demonstrate that the RSAP technique is more effective in displaying GA than the SVP and Sub-RPE Slab. The average of the mean difference between GA and background regions and the GA separability based on SVP, Sub-RPE Slab, and RSAP were 0.129/0.880, 0.238/0.919, and 0.276/0.938, respectively.

CONCLUSIONS

The RSAP technique was more effective for GA visualization than the conventional SVP and Sub-RPE Slab techniques. Our technique decreases choroidal vasculature influence on GA projection images by analyzing the intensity distribution characteristics in sub-RPE regions. The generated GA projection image with the RSAP technique has improved contrast and distinction.

TRANSLATIONAL RELEVANCE

Our method for automated generation of GA projection images from SD-OCT images may improve the visualization of the macular abnormalities and the management of GA.

Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images

PURPOSE

Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT.

METHODS

Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b⁺Prph2^Rd2 /J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation.

RESULTS

Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane.

CONCLUSIONS

Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina.

TRANSLATIONAL RELEVANCE

The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions.

Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra’s algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data.

New ophthalmologic imaging techniques for detection and monitoring of neurodegenerative changes in diabetes: a systematic review

Optical coherence tomography (OCT) of the retina and around the optic nerve head and corneal confocal microscopy (CCM) are non-invasive and repeatable techniques that can quantify ocular neurodegenerative changes in individuals with diabetes. We systematically reviewed studies of ocular neurodegenerative changes in adults with type 1 or type 2 diabetes and noted changes in the retina, the optic nerve head, and the cornea. Of the 30 studies that met our inclusion criteria, 14 used OCT and 16 used CCM to assess ocular neurodegenerative changes. Even in the absence of diabetic retinopathy, several layers in the retina and the mean retinal nerve fibre layer around the optic nerve head were significantly thinner (-5·36 μm [95% CI -7·13 to -3·58]) in individuals with type 2 diabetes compared with individuals without diabetes. In individuals with type 1 diabetes without retinopathy none of the intraretinal layer thicknesses were significantly reduced compared with individuals without diabetes. In the absence of diabetic polyneuropathy, individuals with type 2 diabetes had a lower nerve density (nerve branch density: -1·10/mm(2) [95% CI -4·22 to 2·02]), nerve fibre density: -5·80/mm(2) [-8·06 to -3·54], and nerve fibre length: -4·00 mm/mm(2) [-5·93 to -2·07]) in the subbasal nerve plexus of the cornea than individuals without diabetes. Individuals with type 1 diabetes without polyneuropathy also had a lower nerve density (nerve branch density: -7·74/mm(2) [95% CI -14·13 to -1·34], nerve fibre density: -2·68/mm(2) [-5·56 to 0·20]), and nerve fibre length: -2·58 mm/mm(2) [-3·94 to -1·21]). Ocular neurodegenerative changes are more evident when diabetic retinopathy or polyneuropathy is present. OCT and CCM are potentially useful, in addition to conventional clinical methods, to assess diabetic neurodegenerative changes. Additional research is needed to determine their incremental benefit and to standardise procedures before the application of OCT and CCM in daily practice.

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

PURPOSE

Off-axis acquisition of spectral domain optical coherence tomography (SDOCT) images has been shown to increase total retinal thickness (TRT) measurements. We analyzed the reproducibility of TRT measurements obtained using either the retinal pigment epithelium (RPE) or Bruch's membrane as reference surfaces in off-axis scans intentionally acquired through multiple pupil positions.

METHODS

Five volumetric SDOCT scans of the macula were obtained from one eye of 25 normal subjects. One scan was acquired through a central pupil position, while subsequent scans were acquired through four peripheral pupil positions. The internal limiting membrane, the RPE, and Bruch's membrane were segmented using automated approaches. These volumes were registered to each other and the TRT was evaluated in 9 Early Treatment of Diabetic Retinopathy Study (ETDRS) regions. The reproducibility of the TRT obtained using the RPE was computed using the mean difference, coefficient of variation (CV), and the intraclass correlation coefficient (ICC), and compared to those obtained using Bruch's membrane as the reference surface. A secondary set of 1545 SDOCT scans was also analyzed in order to gauge the incidence of off-axis scans in a typical acquisition environment.

RESULTS

The photoreceptor tips were dimmer in off-axis images, which affected the RPE segmentation. The overall mean TRT difference and CV obtained using the RPE were 7.04 ± 4.31 μm and 1.46%, respectively, whereas Bruch's membrane was 1.16 ± 1.00 μm and 0.32%, respectively. The ICCs at the subfoveal TRT were 0.982 and 0.999, respectively. Forty-one percent of the scans in the secondary set showed large tilts (> 6%).

CONCLUSIONS

RPE segmentation is confounded by its proximity to the interdigitation zone, a structure strongly affected by the optical Stiles-Crawford effect. Bruch's membrane, however, is unaffected leading to a more robust segmentation that is less dependent upon pupil position.

TRANSLATIONAL RELEVANCE

The way in which OCT images are acquired can independently affect the accuracy of automated retinal thickness measurements. Assessment of scan angle in a clinical dataset demonstrates that off-axis scans are common, which emphasizes the need for caution when relying on automated thickness parameters when this component of scan acquisition is not controlled for.

Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Subretinal fluid (SRF) and sub-retinal pigment epithelium (sub-RPE) fluid are signs of AMD and can be detected in optical coherence tomography images. However, manual detection and segmentation of SRFs and sub-RPE fluids are laborious and time consuming. In this paper, a novel pipeline is proposed for automatic detection of SRFs and sub-RPE fluids. First, top and bottom layers of retina are segmented using a graph cut method. Then, a Split Bregman-based segmentation method is used to segment dark regions between layers. These segmented regions are considered as potential fluid candidates, on which a set of features are generated. After that, a random forest classifier is trained to distinguish between the true fluid regions from the falsely detected fluid regions. This method shows reasonable performance in a leave-one-out evaluation using a dataset from 21 patients.

RefMoB, a Reflectivity Feature Model-Based Automated Method for Measuring Four Outer Retinal Hyperreflective Bands in Optical Coherence Tomography

PURPOSE

To validate a model-driven method (RefMoB) of automatically describing the four outer retinal hyperreflective bands revealed by spectral-domain optical coherence tomography (SDOCT), for comparison with histology of normal macula; to report thickness and position of bands, particularly band 2 (ellipsoid zone [EZ], commonly called IS/OS).

METHODS

Foveal and superior perifoveal scans of seven SDOCT volumes of five individuals aged 28 to 69 years with healthy maculas were used (seven eyes for validation, five eyes for measurement). RefMoB determines band thickness and position by a multistage procedure that models reflectivities as a summation of Gaussians. Band thickness and positions were compared with those obtained by manual evaluators for the same scans, and compared with an independent published histological dataset.

RESULTS

Agreement among manual evaluators was moderate. Relative to manual evaluation, RefMoB reported reduced thickness and vertical shifts in band positions in a band-specific manner for both simulated and empirical data. In foveal and perifoveal scans, band 1 was thick relative to the anatomical external limiting membrane, band 2 aligned with the outer one-third of the anatomical IS ellipsoid, and band 3 (IZ, interdigitation of retinal pigment epithelium and photoreceptors) was cleanly delineated.

CONCLUSIONS

RefMoB is suitable for automatic description of the location and thickness of the four outer retinal hyperreflective bands. Initial results suggest that band 2 aligns with the outer ellipsoid, thus supporting its recent designation as EZ. Automated and objective delineation of band 3 will help investigations of structural biomarkers of dark-adaptation changes in aging.

In vivo microvascular network imaging of the human retina combined with an automatic three-dimensional segmentation method

Microvascular network of the retina plays an important role in diagnosis and monitoring of various retinal diseases. We propose a three-dimensional (3-D) segmentation method with intensity-based Doppler variance (IBDV) based on swept-source optical coherence tomography. The automatic 3-D segmentation method is used to obtain seven surfaces of intraretinal layers. The microvascular network of the retina, which is acquired by the IBDV method, can be divided into six layers. The microvascular network of the six individual layers is visualized, and the morphology and contrast images can be improved by using the segmentation method. This method has potential for earlier diagnosis and precise monitoring in retinal vascular diseases.

Comparison of Retinal Thickness Measurements between the Topcon Algorithm and a Graph-Based Algorithm in Normal and Glaucoma Eyes

PURPOSE

To assess the correlation and agreement between the Topcon built-in algorithm and our graph-based algorithm in measuring the total and regional macular thickness for normal and glaucoma subjects.

METHODS

A total of 228 normal eyes and 93 glaucomatous eyes were enrolled in our study. All patients underwent comprehensive ophthalmic examination and Topcon 3D-OCT 2000 scan. One eye was randomly selected for each subject. The thickness of each layer and the total and regional macular thickness on an Early Treatment of Diabetic Retinopathy Study (ETDRS) chart were measured using the Topcon algorithm and our three-dimensional graph-based algorithm. Correlation and agreement analyses between these two algorithms were performed.

RESULTS

Our graph search algorithm exhibited a strong correlation with Topcon algorithm. The macular GCC thickness values for normal and glaucoma subjects ranged from 0.86 to 0.91 and from 0.78 to 0.90, and the regional macular thickness values ranged from 0.79 to 0.96 and 0.70 to 0.95, respectively. Small differences were observed between the Topcon algorithm and our graph-based algorithm. The span of 95% limits of agreement of macular GCC thickness was less than 28 μm in both normal and glaucoma subjects, respectively. These limits of total and regional macular thickness were 15.5 μm and 23.1 μm for normal subjects and 29.1 μm and 46.4 μm for glaucoma subjects, respectively.

CONCLUSION

Our graph-based algorithm exhibited a high degree of agreement with the Topcon algorithm with respect to thickness measurements in normal and glaucoma subjects. Moreover, our graph-based algorithm can segment the retina into more layers than the Topcon algorithm does.

Optical density filters modeling media opacities cause decreased SD-OCT retinal layer thickness measurements with inter- and intra-individual variation

PURPOSE

To assess the effect of media opacities on thickness measurements of the peripapillary retinal nerve fibre layer (pRNFL) and macular inner retinal layer (mIRL) performed with spectral-domain optical coherence tomography (SD-OCT) using a set of filters with known optical density.

METHODS

Spectral-domain optical coherence tomography volume scans of the optic disc and the macular area were performed in 18 healthy volunteers, using Topcon-3DOCT-1000 Mark II. A set of five filters with optical density ranging from 0.04 to 0.69 was used. The correlation was calculated between the percentage change in thickness measurements (%ΔpRNFL and %ΔmIRL) and the change in optical density. All scans and measurements were performed in duplicate by one operator.

RESULTS

Eighteen right eyes of 18 healthy volunteers were included in this study. Percentage decrease in pRNFL and mIRL thickness correlated with change in optical density (Spearman's rho r = 0.82; p < 0.001 and r = 0.89; p < 0.001, respectively). The measured decrease in pRNFL thickness differed from the decrease in mIRL thickness, not only between individuals, but also within the same individual.

CONCLUSIONS

Optical coherence tomography thickness measurements of both pRNFL and mIRL are influenced by image degradation caused by optical density filters as a model for media opacities. An underestimation of the thickness of these layers was observed, caused by a shift of retinal layer boundary placement due to image quality loss. This underestimation is not the same for each individual and also differed between the pRNFL and mIRL thickness measurements. These individual and interindividual differences demonstrate that an individual approach will be necessary to correct for this underestimation per layer.

Automated choroid segmentation based on gradual intensity distance in HD-OCT images

The choroid is an important structure of the eye and plays a vital role in the pathology of retinal diseases. This paper presents an automated choroid segmentation method for high-definition optical coherence tomography (HD-OCT) images, including Bruch's membrane (BM) segmentation and choroidal-scleral interface (CSI) segmentation. An improved retinal nerve fiber layer (RNFL) complex removal algorithm is presented to segment BM by considering the structure characteristics of retinal layers. By analyzing the characteristics of CSI boundaries, we present a novel algorithm to generate a gradual intensity distance image. Then an improved 2-D graph search method with curve smooth constraints is used to obtain the CSI segmentation. Experimental results with 212 HD-OCT images from 110 eyes in 66 patients demonstrate that the proposed method can achieve high segmentation accuracy. The mean choroid thickness difference and overlap ratio between our proposed method and outlines drawn by experts was 6.72µm and 85.04%, respectively.

Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion

Optical coherence tomography (OCT) provides not only morphological information but also information about layer-specific optical intensities, which may represent the underlying tissue properties. The purpose of this study is to quantitatively investigate the optical intensity of each retinal layers in central retinal artery occlusion (CRAO). Twenty-nine CRAO cases at acute phase and 33 normal controls were included. Macula-centered 3D OCT images were segmented with a fully-automated Iowa Reference Algorithm into 10 layers. Layer-specific mean intensities were determined and compared between the patient and control groups using multiple regression analysis while adjusting for age and optical intensity of the entire region. The optical intensities were higher in CRAO than in controls in layers spanning from the retinal ganglion cell layer to outer plexiform layer (standardized beta = 0.657 to 0.777, all p < 0.001), possibly due to ischemia. Optical intensities were lower at the photoreceptor, retinal pigment epithelium (RPE), and choroid layers (standardized beta = −0.412 to −0.611, all p < 0.01), possibly due to shadowing effects. Among the intraretinal layers, the inner nuclear layer was identified as the best indicator of CRAO. Our study provides in vivo information of the optical intensity changes in each retinal layer in CRAO patients.

Comparison of retinal and choriocapillaris thicknesses following sitting to supine transition in healthy individuals and patients with age-related macular degeneration

IMPORTANCE

The effects of position on retinal and choroidal structure are absent from the literature yet may provide insights into disease states such as age-related macular degeneration (AMD).

OBJECTIVE

To evaluate the effect of postural change on retinal and choroidal structures in healthy volunteers and patients with non-neovascular AMD.

DESIGN, SETTING, AND PARTICIPANTS

Prospective observational case series at an academic tertiary care retina service from September 2013 to April 2014 involving 4 unaffected volunteers (8 eyes) and 7 patients (8 eyes) with intermediate AMD. Healthy volunteers selected for the study had no evidence of ocular disease. Patients with AMD were required to have at least 10 intermediate-sized drusen.

EXPOSURES

Spectral-domain optical coherence tomography with enhanced depth imaging in upright (sitting) and supine positions. Stable imaging was achieved using a rotating adjustable mechanical arm that we constructed to allow the optical coherence tomography transducer to rotate 90°. The Iowa Reference Algorithms were used to quantify choroid and choriocapillaris thicknesses.

MAIN OUTCOMES AND MEASURES

Changes in sitting and supine position central macular thickness (in micrometers), total macular volume (in cubic millimeters), choroidal thickness (in micrometers), and choriocapillaris-equivalent thickness (CCET, in micrometers).

RESULTS

Choriocapillaris-equivalent thickness was thinner in healthy participants (9.89 μm; range, 7.15-12.5 μm) compared with patients with intermediate AMD (16.73 μm; range, 10.31-27.38 μm) (P = .02); there was no difference in overall choroidal thickness between the 2 groups (P = .38). There was a 15% CCET reduction among healthy participants when transitioning from a sitting (9.89 μm) to supine (8.4 μm; range, 6.92-10.7 μm) position (P = .02) vs a CCET reduction of 11.1% from sitting (16.73 μm) to supine (14.88 μm; range, 8.76-20.8 μm) positioning (P = .10) in patients with intermediate AMD.

CONCLUSIONS AND RELEVANCE

Intermediate AMD appears to be associated with an increase in CCET and with a lack of positional responses that are observed in the CCET of normal eyes. Our results suggest that although outer portions of the choroid do not appear to be responsive to modest positional or hydrostatic pressure, the choriocapillaris capacity is, and this is measurable in vivo. Whether this physiologic deviation that occurs in AMD is related to atrophy, inflammation, or changes in autoregulatory factors or growth factors remains to be determined.