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

Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We propose a method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset with OCT images from 20 subjects diagnosed with PED. The experimental results showed the following. 1) The overall mean unsigned border positioning error for layer segmentation is 7.87±3.36 μm , and is comparable to the mean inter-observer variability ( 7.81±2.56 μm). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%, respectively. 3) The average running time is 220 s for OCT data of 512 × 64 × 480 voxels.

Longitudinal graph-based segmentation of macular OCT using fundus alignment

Segmentation of retinal layers in optical coherence tomography (OCT) has become an important diagnostic tool for a variety of ocular and neurological diseases. Currently all OCT segmentation algorithms analyze data independently, ignoring previous scans, which can lead to spurious measurements due to algorithm variability and failure to identify subtle changes in retinal layers. In this paper, we present a graph-based segmentation framework to provide consistent longitudinal segmentation results. Regularization over time is accomplished by adding weighted edges between corresponding voxels at each visit. We align the scans to a common subject space before connecting the graphs by registering the data using both the retinal vasculature and retinal thickness generated from a low resolution segmentation. This initial segmentation also allows the higher dimensional temporal problem to be solved more efficiently by reducing the graph size. Validation is performed on longitudinal data from 24 subjects, where we explore the variability between our longitudinal graph method and a cross-sectional graph approach. Our results demonstrate that the longitudinal component improves segmentation consistency, particularly in areas where the boundaries are difficult to visualize due to poor scan quality.

Fast retinal layer segmentation of spectral domain optical coherence tomography images

An approach to segment macular layer thicknesses from spectral domain optical coherence tomography has been proposed. The main contribution is to decrease computational costs while maintaining high accuracy via exploring Kalman filtering, customized active contour, and curve smoothing. Validation on 21 normal volumes shows that 8 layer boundaries could be segmented within 5.8 s with an average layer boundary error <2.35 μm. It has been compared with state-of-the-art methods for both normal and age-related macular degeneration cases to yield similar or significantly better accuracy and is 37 times faster. The proposed method could be a potential tool to clinically quantify the retinal layer boundaries.

Spatio-Temporal Signatures to Predict Retinal Disease Recurrence

We propose a method to predict treatment response patterns based on spatio-temporal disease signatures extracted from longitudinal spectral domain optical coherence tomography (SD-OCT) images. We extract spatio-temporal disease signatures describing the underlying retinal structure and pathology by transforming total retinal thickness maps into a joint reference coordinate system. We formulate the prediction as a multi-variate sparse generalized linear model regression based on the aligned signatures. The algorithm predicts if and when recurrence of the disease will occur in the future. Experiments demonstrate that the model identifies predictive and interpretable features in the spatio-temporal signature. In initial experiments recurrence vs. non-recurrence is predicted with a ROC AuC of 0.99. Based on observed longitudinal morphology changes and a time-to-event based Cox regression model we predict the time to recurrence with a mean absolute error (MAE) of 1.25 months comparing favorably to elastic net regression (1.34 months), demonstrating the benefit of a spatio-temporal survival model.

Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma

PURPOSE

To determine relationships between spectral-domain optical coherence tomography (SD-OCT) derived regional damage to the retinal ganglion cell-axonal complex (RGC-AC) and visual thresholds for each location of the Humphrey 24-2 visual field, in all stages of open-angle glaucoma.

METHODS

Patients with early, moderate, and advanced glaucoma were recruited from a tertiary glaucoma clinic. Humphrey 24-2 and 9-field Spectralis SD-OCT were acquired for each subject. Individual OCT volumes were aligned, nerve fiber layer (NFL), ganglion cell and inner plexiform layers (GCL+IPL) cosegmented. These layers were then partitioned into 54 sectors corresponding to the 24-2 grid. A Support Vector Machine was trained independently for each sector to predict the sector threshold, using these structural properties.

RESULTS

One hundred twenty-two consecutive subjects, 43 early, 39 moderate, and 40 advanced, glaucoma were included (122 eyes). Average correlation coefficient (R) was 0.68 (0.47-0.82), and average root mean square error (RMSE) was 6.92 dB (3.93-8.68 dB). Prediction performance averaged over the entire field, superior hemifield, and inferior hemifield had R (RMSE) values of 0.77 (3.76), 0.80 (5.05), and 0.84 (3.80) dB, respectively.

CONCLUSIONS

Predicting individual 24-2 visual field thresholds from structural information derived from nine-field SD-OCT local NFL and GCL+IPL thicknesses using the RGC-AC concept is feasible, showing the potential for the predictive ability of SD-OCT structural information for visual function. Ultimately, it may be feasible to complement and reduce the burden of subjective visual field testing in glaucoma patients with predicted function derived objectively from OCT.

Accurate and automated image segmentation of 3D optical coherence tomography data suffering from low signal-to-noise levels

Optical coherence tomography (OCT) has proven to be a useful tool for investigating internal structures in ceramic tapes, and the technique is expected to be important for roll-to-roll manufacturing. However, because of high scattering in ceramic materials, noise and speckles deteriorate the image quality, which makes automated quantitative measurements of internal interfaces difficult. To overcome this difficulty we present in this paper an innovative image analysis approach based on volumetric OCT data. The engine in the analysis is a 3D image processing and analysis algorithm. It is dedicated to boundary segmentation and dimensional measurement in volumetric OCT images, and offers high accuracy, efficiency, robustness, subpixel resolution, and a fully automated operation. The method relies on the correlation property of a physical interface and effectively eliminates pixels caused by noise and speckles. The remaining pixels being stored are the ones confirmed to be related to the target interfaces. Segmentation of tilted and curved internal interfaces separated by ∼10  μm in the Z direction is demonstrated. The algorithm also extracts full-field top-view intensity maps of the target interfaces for high-accuracy measurements in the X and Y directions. The methodology developed here may also be adopted in other similar 3D imaging and measurement technologies, e.g., ultrasound imaging, and for various materials.

Multi-surface and multi-field co-segmentation of 3-D retinal optical coherence tomography

When segmenting intraretinal layers from multiple optical coherence tomography (OCT) images forming a mosaic or a set of repeated scans, it is attractive to exploit the additional information from the overlapping areas rather than discarding it as redundant, especially in low contrast and noisy images. However, it is currently not clear how to effectively combine the multiple information sources available in the areas of overlap. In this paper, we propose a novel graph-theoretic method for multi-surface multi-field co-segmentation of intraretinal layers, assuring consistent segmentation of the fields across the overlapped areas. After 2-D en-face alignment, all the fields are segmented simultaneously, imposing a priori soft interfield-intrasurface constraints for each pair of overlapping fields. The constraints penalize deviations from the expected surface height differences, taken to be the depth-axis shifts that produce the maximum cross-correlation of pairwise-overlapped areas. The method's accuracy and reproducibility are evaluated qualitatively and quantitatively on 212 OCT images (20 nine-field, 32 single-field acquisitions) from 26 patients with glaucoma. Qualitatively, the obtained thickness maps show no stitching artifacts, compared to pronounced stitches when the fields are segmented independently. Quantitatively, two ophthalmologists manually traced four intraretinal layers on 10 patients, and the average error ( 4.58 ±1.46 μm) was comparable to the average difference between the observers ( 5.86±1.72 μm). Furthermore, we show the benefit of the proposed approach in co-segmenting longitudinal scans. As opposed to segmenting layers in each of the fields independently, the proposed co-segmentation method obtains consistent segmentations across the overlapped areas, producing accurate, reproducible, and artifact-free results.

Population-based evaluation of retinal nerve fiber layer, retinal ganglion cell layer, and inner plexiform layer as a diagnostic tool for glaucoma

PURPOSE

We determined the glaucoma screening performance of regional optical coherence tomography (OCT) layer thickness measurements in the peripapillary and macular region, in a population-based setting.

METHODS

Subjects (n = 1224) in the Rotterdam Study underwent visual field testing (Humphrey Field Analyzer) and OCT of the macula and optic nerve head (Topcon 3-D OCT-1000). We determined the mean thicknesses of the retinal nerve fiber layer (RNFL), retinal ganglion cell layer (RGCL), and inner plexiform layer for regions-of-interest; thus, defining a series of OCT parameters, using the Iowa Reference Algorithms. Reference standard was the presence of glaucomatous visual field loss (GVFL); controls were subjects without GVFL, an intraocular pressure (IOP) of 21 mm Hg or less, and no positive family history for glaucoma. We calculated the area under the receiver operating characteristics curve (AUCs) and the sensitivity at 97.5% specificity for each parameter.

RESULTS

After excluding 23 subjects with an IOP > 21 mm Hg and 73 subjects with a positive family history for glaucoma, there were 1087 controls and 41 glaucoma cases. Mean RGCL thickness in the inferior half of the macular region showed the highest AUC (0.85; 95% confidence interval [CI] 0.77-0.92) and sensitivity (53.7%; 95% CI, 38.7-68.0%). The mean thickness of the peripapillary RNFL had an AUC of 0.77 (95% CI, 0.69-0.85) and a sensitivity of 24.4% (95% CI, 13.7-39.5%).

CONCLUSIONS

Macular RGCL loss is at least as common as peripapillary RNFL abnormalities in population-based glaucoma cases. Screening for glaucoma using OCT-derived regional thickness identifies approximately half of those cases of glaucoma as diagnosed by perimetry.

Subvoxel accurate graph search using non-Euclidean graph space

Graph search is attractive for the quantitative analysis of volumetric medical images, and especially for layered tissues, because it allows globally optimal solutions in low-order polynomial time. However, because nodes of graphs typically encode evenly distributed voxels of the volume with arcs connecting orthogonally sampled voxels in Euclidean space, segmentation cannot achieve greater precision than a single unit, i.e. the distance between two adjoining nodes, and partial volume effects are ignored. We generalize the graph to non-Euclidean space by allowing non-equidistant spacing between nodes, so that subvoxel accurate segmentation is achievable. Because the number of nodes and edges in the graph remains the same, running time and memory use are similar, while all the advantages of graph search, including global optimality and computational efficiency, are retained. A deformation field calculated from the volume data adaptively changes regional node density so that node density varies with the inverse of the expected cost. We validated our approach using optical coherence tomography (OCT) images of the retina and 3-D MR of the arterial wall, and achieved statistically significant increased accuracy. Our approach allows improved accuracy in volume data acquired with the same hardware, and also, preserved accuracy with lower resolution, more cost-effective, image acquisition equipment. The method is not limited to any specific imaging modality and readily extensible to higher dimensions.

Automated 3D Segmentation of Intraretinal Surfaces in SD-OCT Volumes in Normal and Diabetic Mice

PURPOSE

To describe an adaptation of an existing graph-theoretic method (initially developed for human optical coherence tomography [OCT] images) for the three-dimensional (3D) automated segmentation of 10 intraretinal surfaces in mice scans, and assess the accuracy of the method and the reproducibility of thickness measurements.

METHODS

Ten intraretinal surfaces were segmented in repeat spectral domain (SD)-OCT volumetric images acquired from normal (n = 8) and diabetic (n = 10) mice. The accuracy of the method was assessed by computing the border position errors of the automated segmentation with respect to manual tracings obtained from two experts. The reproducibility was statistically assessed for four retinal layers within eight predefined regions using the mean and SD of the differences in retinal thickness measured in the repeat scans, the coefficient of variation (CV) and the intraclass correlation coefficients (ICC; with 95% confidence intervals [CIs]).

RESULTS

The overall mean unsigned border position error for the 10 surfaces computed over 97 B-scans (10 scans, 10 normal mice) was 3.16 ± 0.91 μm. The overall mean differences in retinal thicknesses computed from the normal and diabetic mice were 1.86 ± 0.95 and 2.15 ± 0.86 μm, respectively. The CV of the retinal thicknesses for all the measured layers ranged from 1.04% to 5%. The ICCs for the total retinal thickness in the normal and diabetic mice were 0.78 [0.10, 0.92] and 0.83 [0.31, 0.96], respectively.

CONCLUSION

The presented method (publicly available as part of the Iowa Reference Algorithms) has acceptable accuracy and reproducibility and is expected to be useful in the quantitative study of intraretinal layers in mice.

TRANSLATIONAL RELEVANCE

The presented method, initially developed for human OCT, has been adapted for mice, with the potential to be adapted for other animals as well. Quantitative in vivo assessment of the retina in mice allows changes to be measured longitudinally, decreasing the need for them.

Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint

Automatic segmentation of retinal layers in spectral domain optical coherence tomography (SD-OCT) images plays a vital role in the quantitative assessment of retinal disease, because it provides detailed information which is hard to process manually. A number of algorithms to automatically segment retinal layers have been developed; however, accurate edge detection is challenging. We developed an automatic algorithm for segmenting retinal layers based on dual-gradient and spatial correlation smoothness constraint. The proposed algorithm utilizes a customized edge flow to produce the edge map and a convolution operator to obtain local gradient map in the axial direction. A valid search region is then defined to identify layer boundaries. Finally, a spatial correlation smoothness constraint is applied to remove anomalous points at the layer boundaries. Our approach was tested on two datasets including 10 cubes from 10 healthy eyes and 15 cubes from 6 patients with age-related macular degeneration. A quantitative evaluation of our method was performed on more than 600 images from cubes obtained in five healthy eyes. Experimental results demonstrated that the proposed method can estimate six layer boundaries accurately. Mean absolute boundary positioning differences and mean absolute thickness differences (mean±SD) were 4.43±3.32 μm and 0.22±0.24 μm, respectively.

User-guided segmentation for volumetric retinal optical coherence tomography images

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Analysis of macular OCT images using deformable registration

Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography

This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.

Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization

With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. This paper presents a novel probabilistic approach, that models the appearance of retinal layers as well as the global shape variations of layer boundaries. Given an OCT scan, the full posterior distribution over segmentations is approximately inferred using a variational method enabling efficient probabilistic inference in terms of computationally tractable model components: Segmenting a full 3-D volume takes around a minute. Accurate segmentations demonstrate the benefit of using global shape regularization: We segmented 35 fovea-centered 3-D volumes with an average unsigned error of 2.46 ± 0.22 μm as well as 80 normal and 66 glaucomatous 2-D circular scans with errors of 2.92 ± 0.5 μm and 4.09 ± 0.98 μm respectively. Furthermore, we utilized the inferred posterior distribution to rate the quality of the segmentation, point out potentially erroneous regions and discriminate normal from pathological scans. No pre- or postprocessing was required and we used the same set of parameters for all data sets, underlining the robustness and out-of-the-box nature of our approach.

Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization

PURPOSE

We reported a fully automated method to identify and quantify the thickness of the outer retinal-subretinal (ORSR) layer from clinical spectral-domain optical coherence tomography (SD-OCT) scans of choroidal neovascularization (CNV) due to exudative age-related macular degeneration (eAMD).

METHODS

A total of 23 subjects with CNV met eligibility. Volumetric SD-OCT scans of 23 eyes were obtained (Zeiss Cirrus, 200 × 200 × 1024 voxels). In a subset of eyes, scans were repeated. The OCT volumes were analyzed using our standard parameters and using a 3-dimensional (3D) graph-search approach with an adaptive cost function. A retinal specialist graded the segmentation as generally accurate, local segmentation inaccuracies, or failure. Reproducibility on repeat scans was analyzed using root mean square coefficient of variation (RMS CV) of the average ORSR thickness.

RESULTS

Using a standard segmentation approach, 1/23 OCT segmentations was graded generally accurate and 22/23 were failure(s). With the adaptive method 21/23 segmentations were graded generally accurate; 2/23 were local segmentation inaccuracies and none was a failure. The intermethod quality of segmentation was significantly different (P << 0.001). The average ORSR thickness measured on CNV patients (78.0 μm; 95% confidence interval [CI], 72.5-83.4 μm) is significantly larger (P << 0.001) than normal average ORSR layer thickness (51.5 ± 3.3 μm). The RMS CV was 8.1%.

CONCLUSIONS

We have developed a fully automated 3D method for segmenting the ORSR layer in SD-OCT of patients with CNV from eAMD. Our method can quantify the ORSR layer thickness in the presence of fluid, which has the potential to augment management accuracy and efficiency of anti-VEGF treatment.

Multiple-object geometric deformable model for segmentation of macular OCT

Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed deformable model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method.

Optical Coherence Tomography (OCT) Device Independent Intraretinal Layer Segmentation

PURPOSE

To develop and test an algorithm to segment intraretinal layers irrespectively of the actual Optical Coherence Tomography (OCT) device used.

METHODS

The developed algorithm is based on the graph theory optimization. The algorithm's performance was evaluated against that of three expert graders for unsigned boundary position difference and thickness measurement of a retinal layer group in 50 and 41 B-scans, respectively. Reproducibility of the algorithm was tested in 30 C-scans of 10 healthy subjects each with the Spectralis and the Stratus OCT. Comparability between different devices was evaluated in 84 C-scans (volume or radial scans) obtained from 21 healthy subjects, two scans per subject with the Spectralis OCT, and one scan per subject each with the Stratus OCT and the RTVue-100 OCT. Each C-scan was segmented and the mean thickness for each retinal layer in sections of the early treatment of diabetic retinopathy study (ETDRS) grid was measured.

RESULTS

The algorithm was able to segment up to 11 intraretinal layers. Measurements with the algorithm were within the 95% confidence interval of a single grader and the difference was smaller than the interindividual difference between the expert graders themselves. The cross-device examination of ETDRS-grid related layer thicknesses highly agreed between the three OCT devices. The algorithm correctly segmented a C-scan of a patient with X-linked retinitis pigmentosa.

CONCLUSIONS

The segmentation software provides device-independent, reliable, and reproducible analysis of intraretinal layers, similar to what is obtained from expert graders.

TRANSLATIONAL RELEVANCE

Potential application of the software includes routine clinical practice and multicenter clinical trials.

Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 μm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness.

Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes

The need to segment multiple interacting surfaces is a common problem in medical imaging and it is often assumed that such surfaces are continuous within the confines of the region of interest. However, in some application areas, the surfaces of interest may contain a shared hole in which the surfaces no longer exist and the exact location of the hole boundary is not known a priori. The boundary of the neural canal opening seen in spectral-domain optical coherence tomography volumes is an example of a "hole" embedded with multiple surrounding surfaces. Segmentation approaches that rely on finding the surfaces alone are prone to failures as deeper structures within the hole can "attract" the surfaces and pull them away from their correct location at the hole boundary. With this application area in mind, we present a graph-theoretic approach for segmenting multiple surfaces with a shared hole. The overall cost function that is optimized consists of both the costs of the surfaces outside the hole and the cost of boundary of the hole itself. The constraints utilized were appropriately adapted in order to ensure the smoothness of the hole boundary in addition to ensuring the smoothness of the non-overlapping surfaces. By using this approach, a significant improvement was observed over a more traditional two-pass approach in which the surfaces are segmented first (assuming the presence of no hole) followed by segmenting the neural canal opening.

An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively.

Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema

PURPOSE

To develop EdgeSelect, a semi-automatic method for the segmentation of retinal layers in spectral domain optical coherence tomography images, and to compare the segmentation results with a manual method.

METHODS

SD-OCT (Heidelberg Spectralis) scans of 28 eyes (24 patients with diabetic macular edema and 4 normal subjects) were imported into a customized MATLAB application, and were manually segmented by three graders at the layers corresponding to the inner limiting membrane (ILM), the inner segment/ellipsoid interface (ISe), the retinal/retinal pigment epithelium interface (RPE), and the Bruch's membrane (BM). The scans were then segmented independently by the same graders using EdgeSelect, a semi-automated method allowing the graders to guide/correct the layer segmentation interactively. The inter-grader reproducibility and agreement in locating the layer positions between the manual and EdgeSelect methods were assessed and compared using the Wilcoxon signed rank test.

RESULTS

The inter-grader reproducibility using the EdgeSelect method for retinal layers varied from 0.15 to 1.21 µm, smaller than those using the manual method (3.36-6.43 µm). The Wilcoxon test indicated the EdgeSelect method had significantly better reproducibility than the manual method. The agreement between the manual and EdgeSelect methods in locating retinal layers ranged from 0.08 to 1.32 µm. There were small differences between the two methods in locating the ILM (p = 0.012) and BM layers (p<0.001), but these were statistically indistinguishable in locating the ISe (p = 0.896) and RPE layers (p = 0.771).

CONCLUSIONS

The EdgeSelect method resulted in better reproducibility and good agreement with a manual method in a set of eyes of normal subjects and with retinal disease, suggesting that this approach is feasible for OCT image analysis in clinical trials.

Semi-automatic geographic atrophy segmentation for SD-OCT images

Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in which the projection region is restricted to a sub-volume of the retina where the presence of GA can be identified. Subsequently, a geometric active contour model is employed to automatically detect and segment the extent of GA in the projection images. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to qualitatively and quantitatively evaluate the proposed GA segmentation method. Experimental results suggest that the proposed algorithm can achieve high segmentation accuracy. The mean GA overlap ratios between our proposed method and outlines drawn in the SD-OCT scans, our method and outlines drawn in the fundus auto-fluorescence (FAF) images, and the commercial software (Carl Zeiss Meditec proprietary software, Cirrus version 6.0) and outlines drawn in FAF images were 72.60%, 65.88% and 59.83%, respectively.

Reproducibility of SD-OCT-based ganglion cell-layer thickness in glaucoma using two different segmentation algorithms

PURPOSE

To compare the reproducibility of spectral-domain optical coherence tomography (SD-OCT)-based ganglion cell-layer-plus-inner plexiform-layer (GCL+IPL) thickness measurements for glaucoma patients obtained using both a publicly available and a commercially available algorithm.

METHODS

Macula SD-OCT volumes (200 × 200 × 1024 voxels, 6 × 6 × 2 mm(3)) were obtained prospectively from both eyes of patients with open-angle glaucoma or with suspected glaucoma on two separate visits within 4 months. The combined GCL+IPL thickness was computed for each SD-OCT volume within an elliptical annulus centered at the fovea, based on two algorithms: (1) a previously published graph-theoretical layer segmentation approach developed at the University of Iowa, and (2) a ganglion cell analysis module of version 6 of Cirrus software. The mean overall thickness of the elliptical annulus was computed as was the thickness within six sectors. For statistical analyses, eyes with an SD-OCT volume with low signal strength (<6), image acquisition errors, or errors in performing the commercial GCL+IPL analysis in at least one of the repeated acquisitions were excluded.

RESULTS

Using 104 eyes (from 56 patients) with repeated measurements, we found the intraclass correlation coefficient for the overall elliptical annular GCL+IPL thickness to be 0.98 (95% confidence interval [CI]: 0.97-0.99) with the Iowa algorithm and 0.95 (95% CI: 0.93-0.97) with the Cirrus algorithm; the intervisit SDs were 1.55 μm (Iowa) and 2.45 μm (Cirrus); and the coefficients of variation were 2.2% (Iowa) and 3.5% (Cirrus), P < 0.0001.

CONCLUSIONS

SD-OCT-based GCL+IPL thickness measurements in patients with early glaucoma are highly reproducible.

Quantitative analysis of retinal layer optical intensities on three-dimensional optical coherence tomography

PURPOSE

To investigate the optical intensities of all retinal layers on three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) in normal subjects using an automatic measurement.

METHODS

Forty normal subjects underwent Topcon 3D OCT-1000 macula-centered scan. The raw data were automatically segmented into 10 layers using the 3D graph search approach. Then the mean and standard deviation of intensities of each layer were calculated. The image quality index was given by the OCT software. Correlation analysis was performed between the optical intensities in each layer and image quality and subject's age.

RESULTS

The correlation of optical intensities was strong from ganglion cell layer (GCL) to outer nuclear layer (ONL) with r > 0.934; moderate among retinal nerve fiber layer (RNFL), photoreceptor, retinal pigment epithelium (RPE), and choroid (0.410 < r < 0.800); and low in the vitreous (0.251 < r < 0.541). The optical intensities were also correlated with the image quality, r > 0.869 from GCL to ONL, 0.748 < r < 0.802 for RNFL, photoreceptor layer, RPE, and the choroid, r = 0.528 for the vitreous. The optical intensity in RNFL was negatively correlated with age (r = -0.365).

CONCLUSIONS

Automatic assessment of the layers' intensities was achieved. In normal subjects, the retinal layers' optical intensities were affected by image quality. Normalization with optical intensity of ONL, all areas, or image quality index is recommended. The optical intensity of RNFL decreased with age.

RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI

BACKGROUND

High-field MRI is a popular technique for the study of rodent brains. These datasets, while similar to human brain MRI in many aspects, present unique image processing challenges. We address a very common preprocessing step, skull-stripping, which refers to the segmentation of the brain tissue from the image for further processing. While several methods exist for addressing this problem, they are computationally expensive and often require interactive post-processing by an expert to clean up poorly segmented areas. This further increases total processing time per subject.

NEW METHOD

We propose a novel algorithm, based on grayscale mathematical morphology and LOGISMOS-based graph segmentation, which is rapid, robust and highly accurate.

RESULTS

Comparative results obtained on two challenging in vivo datasets, consisting of 22 T1-weighted rat brain images and 10 T2-weighted mouse brain images illustrate the robustness and excellent performance of the proposed algorithm, in a fraction of the computational time needed by existing algorithms.

COMPARISON WITH EXISTING METHODS

In comparison to current state-of-the-art methods, our approach achieved average Dice similarity coefficient of 0.92 ± 0.02 and average Hausdorff distance of 13.6 ± 5.2 voxels (vs. 0.85 ± 0.20, p<0.05 and 42.6 ± 22.9, p << 0.001) for the rat dataset, and 0.96 ± 0.01 and average Hausdorff distance of 21.6 ± 12.7 voxels (vs. 0.93 ± 0.01, p <<0.001 and 33.7 ± 3.5, p <<0.001) for the mouse dataset. The proposed algorithm took approximately 90s per subject, compared to 10-20 min for the neural-network based method and 30-90 min for the atlas-based method.

CONCLUSIONS

RATS is a robust and computationally efficient method for accurate rodent brain skull-stripping even in challenging data.

Automated quantification of lung structures from optical coherence tomography images

Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis.

Effect of age on individual retinal layer thickness in normal eyes as measured with spectral-domain optical coherence tomography

PURPOSE

To determine the effect of age on the thickness of individual retinal layers, measured with spectral-domain optical coherence tomography (SD-OCT), in a population of healthy Caucasians.

METHODS

One hundred and twenty subjects with an age ranging between 18 and 81 years were examined with SD-OCT. Mean layer thickness was calculated for seven retinal layers, in the fovea (region 1 of the 9 Early Treatment Diabetic Retinopathy Study [ETDRS] regions); in the pericentral ring (ETDRS regions 2 to 5); and the peripheral ring (ETDRS regions 6 to 9) following automated segmentation using the Iowa Reference Algorithm. In addition, mean peripapillary retinal nerve fiber layer (RNFL) thickness was measured. The partial correlation test was performed on each layer to determine the effect of age on layer thickness, while correcting for spherical equivalent, sex, and Topcon image quality factor as confounders, followed by Bonferroni corrections to adjust for multiple testing.

RESULTS

The thickness of the peripapillary RNFL (R = -0.332; P < 0.001); pericentral ganglion cell layer (R = -0.354, P < 0.001); peripheral inner plexiform layer (R = -0.328, P < 0.001); and foveal outer segment layer (R = -0.381, P < 0.001) decreased significantly with increasing age. Foveal RPE thickness (R = 0.467, P < 0.001) increased significantly with increasing age; other layers showed no significant differences with age.

CONCLUSIONS

Several macular layers and the peripapillary RNFL thickness showed significant changes correlated with age. This should be taken into consideration when analyzing macular layers and the peripapillary RNFL in SD-OCT studies of retinal diseases and glaucoma.

Retinal layer segmentation of macular OCT images using boundary classification

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

Reproducibility of diabetic macular edema estimates from SD-OCT is affected by the choice of image analysis algorithm

PURPOSE

To evaluate the intersession repeatability of retinal thickness measurements in patients with diabetic macular edema (DME) using the Heidelberg Spectralis optical coherence tomography (OCT) algorithm and a publicly available, three-dimensional graph search-based multilayer OCT segmentation algorithm, the Iowa Reference Algorithm.

METHODS

Thirty eyes from 21 patients diagnosed with clinically significant DME were included and underwent consecutive, registered macula-centered spectral-domain optical coherence scans (Heidelberg Spectralis). The OCT scans were segmented into separate surfaces, and the average thickness between internal limiting membrane and outer retinal pigment epithelium complex surfaces was determined using the Iowa Reference Algorithm. Variability between paired scans was analyzed and compared with the retinal thickness obtained from the manufacturer-supplied Spectralis software.

RESULTS

The coefficient of repeatability (variation) for central macular thickness using the Iowa Reference Algorithm was 5.26 μm (0.62% [95% confidence interval (CI), 0.43-0.71]), while for the Spectralis algorithm this was 6.84 μm (0.81% [95% CI, 0.55-0.92]). When the central 3 mm was analyzed, the coefficient of repeatability (variation) was 2.46 μm (0.31% [95% CI, 0.23-0.38]) for the Iowa Reference Algorithm and 4.23 μm (0.53% [95% CI, 0.39-0.65]) for the Spectralis software.

CONCLUSIONS

The Iowa Reference Algorithm and the Spectralis software provide excellent reproducibility between serial scans in patients with clinically significant DME. The publicly available Iowa Reference Algorithm may have lower between-measurement variation than the manufacturer-supplied Spectralis software for the central 3 mm subfield. These findings have significant implications for the management of patients with DME.

Advances in the Structural Evaluation of Glaucoma with Optical Coherence Tomography

Optical coherence tomography (OCT) is capable of providing quantitative and objective assessments of the optic disc, macula and retinal nerve fiber layer in glaucoma. The recent advent of spectral domain OCT (SD-OCT) has enhanced the resolution, decreased scan acquisition time, and improved the reproducibility of measurements compared to older versions of this technology. However, although OCT has been successfully used for detection of disease and evaluation of progression, the limited agreement between structural and functional tests indicates the strong need for a combined approach for detecting and monitoring the disease. A recently described approach for estimation of rates of retinal ganglion cell loss from a combination of SD-OCT and functional data is a promising method for diagnosing, staging, detecting progression, and estimating rates of glaucomatous deterioration.

Human photoreceptor outer segments shorten during light adaptation

PURPOSE

Best disease is a macular dystrophy caused by mutations in the BEST1 gene. Affected individuals exhibit a reduced electro-oculographic (EOG) response to changes in light exposure and have significantly longer outer segments (OS) than age-matched controls. The purpose of this study was to investigate the anatomical changes in the outer retina during dark and light adaptation in unaffected and Best disease subjects, and to compare these changes to the EOG.

METHODS

Unaffected (n = 11) and Best disease patients (n = 7) were imaged at approximately 4-minute intervals during an approximately 40-minute dark-light cycle using spectral domain optical coherence tomography (SD-OCT). EOGs of two subjects were obtained under the same conditions. Automated three-dimensional (3-D) segmentation allowed measurement of light-related changes in the distances between five retinal surfaces.

RESULTS

In normal subjects, there was a significant decrease in outer segment equivalent length (OSEL) of -2.14 μm (95% confidence interval [CI], -1.77 to -2.51 μm) 10 to 20 minutes after the start of light adaptation, while Best disease subjects exhibited a significant increase in OSEL of 2.07 μm (95% CI, 1.79-2.36 μm). The time course of the change in OS length corresponded to that of the EOG waveform.

CONCLUSIONS

Our results strongly suggest that the light peak phase of the EOG is temporally related to a decreased OSEL in normal subjects, and the lack of a light peak phase in Best disease subjects is associated with an increase in OSEL. One potential role of Bestrophin-1 is to trigger an increase in the standing potential that approximates the OS to the apical surface of the RPE to facilitate phagocytosis.

Segmentation of retinal OCT images using a random forest classifier

Optical coherence tomography (OCT) has become one of the most common tools for diagnosis of retinal abnormalities. Both retinal morphology and layer thickness can provide important information to aid in the differential diagnosis of these abnormalities. Automatic segmentation methods are essential to providing these thickness measurements since the manual delineation of each layer is cumbersome given the sheer amount of data within each OCT scan. In this work, we propose a new method for retinal layer segmentation using a random forest classifier. A total of seven features are extracted from the OCT data and used to simultaneously classify nine layer boundaries. Taking advantage of the probabilistic nature of random forests, probability maps for each boundary are extracted and used to help refine the classification. We are able to accurately segment eight retinal layers with an average Dice coefficient of 0.79 ± 0.13 and a mean absolute error of 1.21 ± 1.45 pixels for the layer boundaries.

Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Optimal multiple surface segmentation with shape and context priors

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ±1.58 μ m) was improved to 5.14±0.99 μ m when employing our new method with shape and context priors.

A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes

Optical coherence tomography is routinely used clinically for the detection and management of ocular diseases as well as in research where the studies may involve animals. This routine use requires that the developed automated segmentation methods not only be accurate and reliable, but also be adaptable to meet new requirements. We have previously proposed the use of a graph-theoretic approach for the automated 3-D segmentation of multiple retinal surfaces in volumetric human SD-OCT scans. The method ensures the global optimality of the set of surfaces with respect to a cost function. Cost functions have thus far been typically designed by hand by domain experts. This difficult and time-consuming task significantly impacts the adaptability of these methods to new models. Here, we describe a framework for the automated machine-learning based design of the cost function utilized by this graph-theoretic method. The impact of the learned components on the final segmentation accuracy are statistically assessed in order to tailor the method to specific applications. This adaptability is demonstrated by utilizing the method to segment seven, ten and five retinal surfaces from SD-OCT scans obtained from humans, mice and canines, respectively. The overall unsigned border position errors observed when using the recommended configuration of the graph-theoretic method was 6.45 ± 1.87 μm, 3.35 ± 0.62 μm and 9.75 ± 3.18 μm for the human, mouse and canine set of images, respectively.

A generative model for OCT retinal layer segmentation by integrating graph-based multi-surface searching and image registration

We proposed a generative probabilistic modeling framework for automated segmentation of retinal layers from Optical Coherence Tomography (OCT) data. The objective is to learn a segmentation protocol from a collection of training images that have been manually labeled. Our model results in a novel OCT retinal layer segmentation approach which integrates algorithms of simultaneous searching of multiple interacting layer interfaces, image registration and machine learning. Different from previous work, our approach combines the benefits of constraining spatial layout of retinal layers, using a set of more robust local image descriptors, employing a mechanism for learning from manual labels and incorporating the inter-subject anatomical similarities of retina. With a set of OCT volumetric images from mutant canine retinas, we experimentally validated that our approach outperforms two state-of-the-art techniques.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye's normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Optimizing the information yield of 3-D OCT in glaucoma

PURPOSE

To determine, first, which regions of 3-D optical coherence tomography (OCT) volumes can be segmented completely in the majority of subjects and, second, the relationship between analyzed area and thickness measurement test-retest variability.

METHODS

Three-dimensional OCT volumes (6 × 6 mm) centered around the fovea and optic nerve head (ONH) of 925 Rotterdam Study participants were analyzed; 44 participants were scanned twice. Volumes were segmented into 10 layers, and we determined the area where all layers could be identified in at least 95% (macula) or 90% (ONH) of subjects. Macular volumes were divided in 2 × 2, 4 × 4, 6 × 6, 8 × 8, or 68 blocks. We placed two circles around the ONH; the ONH had to fit into the smaller circle, and the larger circle had to fit into the segmentable part of the volume. The area between the circles was divided in 3 to 12 segments. We determined the test-retest variability (coefficient of repeatability) of the retinal nerve fiber layer (RNFL) and ganglion cell layer (RGCL) thickness measurements as a function of size of blocks/segments.

RESULTS

Eighty-two percent of the macular volume could be segmented in at least 95% of subjects; for the ONH, this was 65% in at least 90%. The radii of the circles were 1.03 and 1.84 mm. Depending on the analyzed area, median test-retest variability ranged from 8% to 15% for macular RNFL, 11% to 22% for macular RGCL, 5% to 11% for the two together, and 18% to 22% for ONH RNFL.

CONCLUSIONS

Test-retest variability hampers a detailed analysis of 3-D OCT data. Combined macular RNFL and RGCL thickness averaged over larger areas had the best test-retest variability.

Quantification of external limiting membrane disruption caused by diabetic macular edema from SD-OCT

PURPOSE

Disruption of external limiting membrane (ELM) integrity on spectral-domain optical coherence tomography (SD-OCT) is associated with lower visual acuity outcomes in patients suffering from diabetic macular edema (DME). However, no automated methods to detect ELM and/or determine its integrity from SD-OCT exist.

METHODS

Sixteen subjects diagnosed with clinically significant DME (CSME) were included and underwent macula-centered SD-OCT (512 × 19 × 496 voxels). Sixteen subjects without retinal thickening and normal acuity were also scanned (200 × 200 × 1024 voxels). Automated quantification of ELM disruption was achieved as follows. First, 11 surfaces were automatically segmented using our standard 3-D graph-search approach, and the subvolume between surface 6 and 11 containing the ELM region was flattened based on the segmented retinal pigment epithelium (RPE) layer. A second, edge-based graph-search surface-detection method segmented the ELM region in close proximity "above" the RPE, and each ELM A-scan was classified as disrupted or nondisrupted based on six texture features in the vicinity of the ELM surface. The vessel silhouettes were considered in the disruption classification process to avoid false detections of ELM disruption.

RESULTS

In subjects with CSME, large areas of disrupted ELM were present. In normal subjects, ELM was largely intact. The mean and 95% confidence interval (CI) of the detected disruption area volume for normal and CSME subjects were mean(normal) = 0.00087 mm(3) and CI(normal) = (0.00074, 0.00100), and mean(CSME) = 0.00461 mm(3) and CI(CSME) = (0.00347, 0.00576) mm(3), respectively.

CONCLUSIONS

In this preliminary study, we were able to show that automated quantification of ELM disruption is feasible and can differentiate continuous ELM in normal subjects from disrupted ELM in subjects with CSME. We have started determining the relationships of quantitative ELM disruption markers to visual outcome in patients undergoing treatment for CSME.

Distribution of damage to the entire retinal ganglion cell pathway: quantified using spectral-domain optical coherence tomography analysis in patients with glaucoma

OBJECTIVES To test the hypothesis that the amount and distribution of glaucomatous damage along the entire retinal ganglion cell-axonal complex (RGC-AC) can be quantified and to map the RGC-AC connectivity in early glaucoma using automated image analysis of standard spectral-domain optical coherence tomography. METHODS Spectral-domain optical coherence tomography volumes were obtained from 116 eyes in 58 consecutive patients with glaucoma or suspected glaucoma. Layer and optic nerve head (ONH) analysis was performed; the mean regional retinal ganglion cell layer thickness (68 regions), nerve fiber layer (NFL) thickness (120 regions), and ONH rim area (12 wedge-shaped regions) were determined. Maps of RGC-AC connectivity were created using maximum correlation between regions' ganglion cell layer thickness, NFL thickness, and ONH rim area; for retinal nerve fiber bundle regions, the maximum "thickness correlation paths" were determined. RESULTS The mean (SD) NFL thickness and ganglion cell layer thickness across all macular regions were 22.5 (7.5) μm and 33.9 (8.4) μm, respectively. The mean (SD) rim area across all ONH wedge regions was 0.038 (0.004) mm2. Connectivity maps were obtained successfully and showed typical nerve fiber bundle connectivity of the RGC-AC cell body segment to the initial NFL axonal segment, of the initial to the final RGC-AC NFL axonal segments, of the final RGC-AC NFL axonal to the ONH axonal segment, and of the RGC-AC cell body segment to the ONH axonal segment. CONCLUSIONS In early glaucoma, the amount and distribution of glaucomatous damage along the entire RGC-AC can be quantified and mapped using automated image analysis of standard spectral-domain optical coherence tomography. Our findings should contribute to better detection and improved management of glaucoma.

The shape of the ganglion cell plus inner plexiform layers of the normal human macula

PURPOSE

To use surfaces generated by two-dimensional penalized splines (2D P-splines) to characterize the shape of the macular ganglion cell plus inner plexiform layers (GCL+IPL) in a group of normal humans.

METHODS

Macular images of the right eyes of 23 normal subjects ranging in age from 18 to 75 years were obtained with spectral-domain optical coherence tomography (SD-OCT). The thickness of GCL+IPL was determined by manual segmentation, areas with blood vessels were removed, and the resulting maps were fit by smooth surfaces in polar coordinates centered on the fovea.

RESULTS

Smooth surfaces based on 2D P-splines could precisely represent GCL+IPL thickness data, with errors comparable to the axial resolution of the SD-OCT instrument. Metrics were developed for the size, shape, and slope of the edge of the foveal depression and size and shape of the surrounding macular ridge. The slope of the foveal edge was negatively correlated with foveal size (r = -0.60). The size of the macular ridge was positively correlated with foveal size (r = 0.75), with a slope near unity (0.90 ± 0.18). The centroids of the foveal edge and macular ridge clustered near the foveal center. The foveal edge and macular ridge were well fit by ellipses. The mean GCL+IPL thickness formed an elliptical annulus elongated by approximately 30% in the horizontal direction.

CONCLUSIONS

The methods developed here provide precise characterization of retinal layers for the study of glaucoma, foveal development, and other applications.

Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography

Segmenting retinal vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging due to the projected neural canal opening (NCO) and relatively low visibility in the ONH center. Color fundus photographs provide a relatively high vessel contrast in the region inside the NCO, but have not been previously used to aid the SD-OCT vessel segmentation process. Thus, in this paper, we present two approaches for the segmentation of retinal vessels in SD-OCT volumes that each take advantage of complimentary information from fundus photographs. In the first approach (referred to as the registered-fundus vessel segmentation approach), vessels are first segmented on the fundus photograph directly (using a k-NN pixel classifier) and this vessel segmentation result is mapped to the SD-OCT volume through the registration of the fundus photograph to the SD-OCT volume. In the second approach (referred to as the multimodal vessel segmentation approach), after fundus-to-SD-OCT registration, vessels are simultaneously segmented with a k -NN classifier using features from both modalities. Three-dimensional structural information from the intraretinal layers and neural canal opening obtained through graph-theoretic segmentation approaches of the SD-OCT volume are used in combination with Gaussian filter banks and Gabor wavelets to generate the features. The approach is trained on 15 and tested on 19 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 34 subjects with glaucoma. Based on a receiver operating characteristic (ROC) curve analysis, the present registered-fundus and multimodal vessel segmentation approaches [area under the curve (AUC) of 0.85 and 0.89, respectively] both perform significantly better than the two previous OCT-based approaches (AUC of 0.78 and 0.83, p < 0.05). The multimodal approach overall performs significantly better than the other three approaches (p < 0.05).

Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search

In this paper, we present an automatic renal cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of renal cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the renal cortex and renal column. Third, a renal cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (D(avg)), and average symmetric rms surface distance (D(rms)). The experimental results of DSC, OE, SVD, D(avg) , and D(rms) were 90.50% ± 1.19%, 4.38% ± 3.93%, 2.37% ± 1.72%, 0.14 mm ± 0.09 mm , and 0.80 mm ± 0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method.

Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut

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

Automated quantification of volumetric optic disc swelling in papilledema using spectral-domain optical coherence tomography

PURPOSE

To develop an automated method for the quantification of volumetric optic disc swelling in papilledema subjects using spectral-domain optical coherence tomography (SD-OCT) and to determine the extent that such volumetric measurements correlate with Frisén scale grades (from fundus photographs) and two-dimensional (2-D) peripapillary retinal nerve fiber layer (RNFL) and total retinal (TR) thickness measurements from SD-OCT.

METHODS

A custom image-analysis algorithm was developed to obtain peripapillary circular RNFL thickness, TR thickness, and TR volume measurements from SD-OCT volumes of subjects with papilledema. In addition, peripapillary RNFL thickness measures from the commercially available Zeiss SD-OCT machine were obtained. Expert Frisén scale grades were independently obtained from corresponding fundus photographs.

RESULTS

In 71 SD-OCT scans, the mean (± standard deviation) resulting TR volumes for Frisén scale 0 to scale 4 were 11.36 ± 0.56, 12.53 ± 1.21, 14.42 ± 2.11, 17.48 ± 2.63, and 21.81 ± 3.16 mm(3), respectively. The Spearman's rank correlation coefficient was 0.737. Using 55 eyes with valid Zeiss RNFL measurements, Pearson's correlation coefficient (r) between the TR volume and the custom algorithm's TR thickness, the custom algorithm's RNFL thickness, and Zeiss' RNFL thickness was 0.980, 0.929, and 0.946, respectively. Between Zeiss' RNFL and the custom algorithm's RNFL, and the study's TR thickness, r was 0.901 and 0.961, respectively.

CONCLUSIONS

Volumetric measurements of the degree of disc swelling in subjects with papilledema can be obtained from SD-OCT volumes, with the mean volume appearing to be roughly linearly related to the Frisén scale grade. Using such an approach can provide a more continuous, objective, and robust means for assessing the degree of disc swelling compared with presently available approaches.