Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images

Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

A review of algorithms for segmentation of optical coherence tomography from retina

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging.

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.

Automated segmentation algorithm for detection of changes in vaginal epithelial morphology using optical coherence tomography

We have explored the use of optical coherence tomography (OCT) as a noninvasive tool for assessing the toxicity of topical microbicides, products used to prevent HIV, by monitoring the integrity of the vaginal epithelium. A novel feature-based segmentation algorithm using a nearest-neighbor classifier was developed to monitor changes in the morphology of vaginal epithelium. The two-step automated algorithm yielded OCT images with a clearly defined epithelial layer, enabling differentiation of normal and damaged tissue. The algorithm was robust in that it was able to discriminate the epithelial layer from underlying stroma as well as residual microbicide product on the surface. This segmentation technique for OCT images has the potential to be readily adaptable to the clinical setting for noninvasively defining the boundaries of the epithelium, enabling quantifiable assessment of microbicide-induced damage in vaginal tissue.

Automated segmentation of the choroid from clinical SD-OCT

PURPOSE

We developed and evaluated a fully automated 3-dimensional (3D) method for segmentation of the choroidal vessels, and quantification of choroidal vasculature thickness and choriocapillaris-equivalent thickness of the macula, and evaluated repeat variability in normal subjects using standard clinically available spectral domain optical coherence tomography (SD-OCT).

METHODS

A total of 24 normal subjects was imaged twice, using clinically available, 3D SD-OCT. A novel, fully-automated 3D method was used to segment and visualize the choroidal vasculature in macular scans. Local choroidal vasculature and choriocapillaris-equivalent thicknesses were determined. Reproducibility on repeat imaging was analyzed using overlapping rates, Dice coefficient, and root mean square coefficient of variation (CV) of choroidal vasculature and choriocapillaris-equivalent thicknesses.

RESULTS

For the 6 × 6 mm(2) macula-centered region as depicted by the SD-OCT, average choroidal vasculature thickness in normal subjects was 172.1 μm (95% confidence interval [CI] 163.7-180.5 μm) and average choriocapillaris-equivalent thickness was 23.1 μm (95% CI 20.0-26.2 μm). Overlapping rates were 0.79 ± 0.07 and 0.75 ± 0.06, Dice coefficient was 0.78 ± 0.08, CV of choroidal vasculature thickness was 8.0% (95% CI 6.3%-9.4%), and of choriocapillaris-equivalent thickness was 27.9% (95% CI 21.0%-33.3%).

CONCLUSIONS

Fully automated 3D segmentation and quantitative analysis of the choroidal vasculature and choriocapillaris-equivalent thickness demonstrated excellent reproducibility in repeat scans (CV 8.0%) and good reproducibility of choriocapillaris-equivalent thickness (CV 27.9%). Our method has the potential to improve the diagnosis and management of patients with eye diseases in which the choroid is affected.

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).

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.

Quantitative evaluation of papilledema from stereoscopic color fundus photographs

PURPOSE

To derive a computerized measurement of optic disc volume from digital stereoscopic fundus photographs for the purpose of diagnosing and managing papilledema.

METHODS

Twenty-nine pairs of stereoscopic fundus photographs and optic nerve head (ONH) centered spectral domain optical coherence tomography (SD-OCT) scans were obtained at the same visit in 15 patients with papilledema. Some patients were imaged at multiple visits in order to assess their changes. Three-dimensional shape of the ONH was estimated from stereo fundus photographs using an automated multi-scale stereo correspondence algorithm. We assessed the correlation of the stereo volume measurements with the SD-OCT volume measurements quantitatively, in terms of volume of retinal surface elevation above a reference plane and also to expert grading of papilledema from digital fundus photographs using the Frisén grading scale.

RESULTS

The volumetric measurements of retinal surface elevation estimated from stereo fundus photographs and OCT scans were positively correlated (correlation coefficient r(2) = 0.60; P < 0.001) and were positively correlated with Frisén grade (Spearman correlation coefficient r = 0.59; P < 0.001).

CONCLUSIONS

Retinal surface elevation among papilledema patients obtained from stereo fundus photographs compares favorably with that from OCT scans and with expert grading of papilledema severity. Stereoscopic color imaging of the ONH combined with a method of automated shape reconstruction is a low-cost alternative to SD-OCT scans that has potential for a more cost-effective diagnosis and management of papilledema in a telemedical setting. An automated three-dimensional image analysis method was validated that quantifies the retinal surface topography with an imaging modality that has lacked prior objective assessment.

Characterization of structure and function of the mouse retina using pattern electroretinography, pupil light reflex, and optical coherence tomography

OBJECTIVE

To perform in vivo analysis of retinal functional and structural parameters in healthy mouse eyes.

ANIMAL STUDIED

Adult C57BL/6 male mice (n = 37).

PROCEDURES

Retinal function was evaluated using pattern electroretinography (pERG) and the chromatic pupil light reflex (cPLR). Structural properties of the retina and nerve fiber layer (NFL) were evaluated using spectral-domain optical coherence tomography (SD-OCT).

RESULTS

The average pERG amplitudes were found to be 11.2 ± 0.7 μV (P50-N95, mean ± SEM), with an implicit time for P50-N95 interval of 90.4 ± 5.4 ms. Total retinal thickness was 229.5 ± 1.7 μm (mean ± SEM) in the area centralis region. The thickness of the retinal nerve fiber layer (mean ± SEM) using a circular peripapillary retinal scan centered on the optic nerve was 46.7 ± 0.9 μm (temporal), 46.1 ± 0.9 μm (superior), 45.8 ± 0.9 μm (nasal), and 48.4 ± 1 μm (inferior). The baseline pupil diameter was 2.1 ± 0.05 mm in darkness, and 1.1 ± 0.05 and 0.56 ± 0.03 mm after stimulation with red (630 nm, luminance 200 kcd/m(2)) or blue (480 nm, luminance 200 kcd/m(2)) light illumination, respectively.

CONCLUSIONS

Pattern electroretinography, cPLR and SD-OCT analysis are reproducible techniques, which can provide important information about retinal and optic nerve function and structure in mice.

Early neurodegeneration in the retina of type 2 diabetic patients

PURPOSE

The purpose of this study was to determine whether diabetes type 2 causes thinning of retinal layers as a sign of neurodegeneration and to investigate the possible relationship between this thinning and duration of diabetes mellitus, diabetic retinopathy (DR) status, age, sex, and glycemic control (HbA1c).

METHODS

Mean layer thickness was calculated for retinal layers following automated segmentation of spectral domain optical coherence tomography images of diabetic patients with no or minimal DR and compared with controls. To determine the relationship between layer thickness and diabetes duration, DR status, age, sex, and HbA1c, a multiple linear regression analysis was used.

RESULTS

In the pericentral area of the macula, the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL) were thinner in patients with minimal DR compared to controls (respective difference 1.9 μm, 95% confidence interval [CI] 0.3-3.5 μm; 5.2 μm, 95% CI 1.0-9.3 μm; 4.5 μm, 95% CI 2.2-6.7 μm). In the peripheral area of the macula, the RNFL and IPL were thinner in patients with minimal DR compared to controls (respective difference 3.2 μm, 95% CI 0.1-6.4 μm; 3.3 μm, 95% CI 1.2-5.4 μm). Multiple linear regression analysis showed DR status to be the only significant explanatory variable (R = 0.31, P = 0.03) for this retinal thinning.

CONCLUSIONS

This study demonstrated thinner inner retinal layers in the macula of type 2 diabetic patients with minimal DR than in controls. These results support the concept that early DR includes a neurodegenerative component.

The Nature of Macular Damage in Glaucoma as Revealed by Averaging Optical Coherence Tomography Data

PURPOSE

To better understand the nature of glaucomatous damage, especially to the macula, the inner retinal thickness maps obtained with frequency domain optical coherence tomography (fdOCT) were averaged.

METHODS

Frequency domain optical coherence tomography macular and optic disc cube scans were obtained from 54 healthy eyes and 156 eyes with glaucomatous optic neuropathy. A manually corrected algorithm was used for layer segmentation. Patients' eyes were grouped both by mean deviation (MD) and hemifield classification using standard categories and 24-2 (6° grid) visual fields (VFs). To obtain average difference maps, the thickness of retinal nerve fiber (RNF) and retinal ganglion cell plus inner plexiform (RGC+) layers were averaged and subtracted from the average control values.

RESULTS

On the average difference maps, RGC+ and RNF layer thinning was seen in the patient groups with VFs classified as normal. The pattern of the thinning was the same, but the degree of thinning increased with decreased MD and with classification category (from normal to arcuate). This RGC+ thinning was largely within the central four points of the 24-2 (6° grid) field, after correcting for RGC displacement.

CONCLUSION

1. VF categories represent different degrees of the same pattern of RGC+ and RNFL layer thinning. 2. RGC+ damage occurs in the central macula even in patients with VFs classified as normal. 3. The 6° grid (24-2) pattern is not optimally designed to detect macular damage. 4. A schematic model of RGC projections is proposed to explain the pattern of macular loss, including the greater vulnerability of the inferior retinal region.

TRANSLATIONAL RELEVANCE

The 24-2 is not an optimal test pattern for detecting or following glaucomatous damage. Further, we suggest clinical fdOCT reports include RGC+ and RNFL probability plots combined with VF information.

Wavelet denoising of multiframe optical coherence tomography data

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.

Role of the macular optical coherence tomography scan in neuro-ophthalmology

BACKGROUND

Recent improvements in optical coherence tomographic (OCT) resolution and automated segmentation software have provided a means of relating visual pathway damage to structural changes in the retinal nerve fiber layer (RNFL) and corresponding soma of the ganglion cells in the inner layers of the macula and also in the outer photoreceptor layer in the macula.

EVIDENCE ACQUISITION

Studies correlating retinal structure with function are reviewed in the context of OCT in optic nerve and retinal disorders.

RESULTS

Recently published work provides evidence showing a strong relationship not only between the RNFL and visual threshold in optic nerve disorders but also between visual sensitivity and the inner layers of the retina in the macula, where the cell bodies of ganglion cells reside. Acquired and genetic disorders affecting the outer retina show correlation between visual sensitivity and the thickness of the outer photoreceptors. These relationships help localize unknown causes of visual field loss through segmentation of the retinal layers using spectral domain OCT.

CONCLUSIONS

Advances in relating the structure of the ganglion cell layer in the macula to the corresponding axons in the RNFL and to visual function further our ability to differentiate and localize ambiguous causes of vision loss and visual field defects in neuro-ophthalmology. Ganglion cell layer analysis in volume OCT data may provide yet another piece of the puzzle to understanding structure-function relationships and its application to diagnosis and monitoring of optic nerve diseases, while similar structure-function relationships are also being elucidated in the outer retina for photoreceptor diseases.

Automated segmentation of intraretinal cystoid fluid in optical coherence tomography

Cystoid macular edema (CME) is observed in a variety of ocular disorders and is strongly associated with vision loss. Optical coherence tomography (OCT) provides excellent visualization of cystoid fluid, and can assist clinicians in monitoring the progression of CME. Quantitative tools for assessing CME may lead to better metrics for choosing treatment protocols. To address this need, this paper presents a fully automated retinal cyst segmentation technique for OCT image stacks acquired from a commercial scanner. The proposed method includes a computationally fast bilateral filter for speckle denoising while maintaining CME boundaries. The proposed technique was evaluated in images from 16 patients with vitreoretinal disease and three controls. The average sensitivity and specificity for the classification of cystoid regions in CME patients were found to be 91% and 96%, respectively, and the retinal volume occupied by cystoid fluid obtained by the algorithm was found to be accurate within a mean and median volume fraction of 1.9% and 0.8%, respectively.

Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model

A two stage statistical model based on texture and shape for fully automatic choroidal segmentation of normal and pathologic eyes obtained by a 1060 nm optical coherence tomography (OCT) system is developed. A novel dynamic programming approach is implemented to determine location of the retinal pigment epithelium/ Bruch's membrane /choriocapillaris (RBC) boundary. The choroid-sclera interface (CSI) is segmented using a statistical model. The algorithm is robust even in presence of speckle noise, low signal (thick choroid), retinal pigment epithelium (RPE) detachments and atrophy, drusen, shadowing and other artifacts. Evaluation against a set of 871 manually segmented cross-sectional scans from 12 eyes achieves an average error rate of 13%, computed per tomogram as a ratio of incorrectly classified pixels and the total layer surface. For the first time a fully automatic choroidal segmentation algorithm is successfully applied to a wide range of clinical volumetric OCT data.

Robust multiscale stereo matching from fundus images with radiometric differences

A robust multiscale stereo matching algorithm is proposed to find reliable correspondences between low contrast and weakly textured retinal image pairs with radiometric differences. Existing algorithms designed to deal with piecewise planar surfaces with distinct features and Lambertian reflectance do not apply in applications such as 3D reconstruction of medical images including stereo retinal images. In this paper, robust pixel feature vectors are formulated to extract discriminative features in the presence of noise in scale space, through which the response of low-frequency mechanisms alter and interact with the response of high-frequency mechanisms. The deep structures of the scene are represented with the evolution of disparity estimates in scale space, which distributes the matching ambiguity along the scale dimension to obtain globally coherent reconstructions. The performance is verified both qualitatively by face validity and quantitatively on our collection of stereo fundus image sets with ground truth, which have been made publicly available as an extension of standard test images for performance evaluation.

Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma

PURPOSE

To demonstrate the capability of SD-OCT to measure macular retinal ganglion cell-inner plexiform layer (GCIPL) thickness and to assess its reproducibility in glaucomatous eyes.

METHODS

Fifty-one glaucomatous eyes (26 mild, 11 moderate, 14 severe) of 51 patients underwent macular scanning using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) macula 200×200 acquisition protocol. Five scans were obtained on 5 days within 2 months. The ganglion cell analysis (GCA) algorithm was used to detect the macular GCIPL and to measure the thickness of the overall average, minimum, superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal GCIPL. The reproducibility of the measurements was evaluated with intraclass correlation coefficients (ICCs), coefficients of variation (COVs), and test-retest standard deviations (TRTSDs).

RESULTS

Segmentation and measurement of GCIPL thickness were successful in 50 of 51 subjects. All ICCs ranged between 0.94 and 0.98, but ICCs for average and superior GCIPL parameters (0.97-0.98) were slightly higher than for inferior GCIPL parameters (0.94-0.97). All COVs were <5%, with 1.8% for average GCIPL and COVs for superior GCIPL parameters (2.2%-3.0%) slightly lower than those for inferior GCIPL parameters (2.5%-3.6%). The TRTSD was lowest for average GCIPL (1.16 μm) and varied from 1.43 to 2.15 μm for sectoral GCIPL CONCLUSIONS: The Cirrus HD-OCT GCA algorithm can successfully segment macular GCIPL and measure GCIPL thickness with excellent intervisit reproducibility. Longitudinal monitoring of GCIPL thickness may be possible with Cirrus HD-OCT for assessing glaucoma progression.

Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa

To provide a tool for quantifying the effects of retinitis pigmentosa (RP) seen on spectral domain optical coherence tomography images, an automated layer segmentation algorithm was developed. This algorithm, based on dual-gradient information and a shortest path search strategy, delineates the inner limiting membrane and three outer retinal boundaries in optical coherence tomography images from RP patients. In addition, an automated inner segment (IS)/outer segment (OS) contour detection method based on the segmentation results is proposed to quantify the locus of points at which the OS thickness goes to zero in a 3D volume scan. The segmentation algorithm and the IS/OS contour were validated with manual segmentation data. The segmentation and IS/OS contour results on repeated measures showed good within-day repeatability, while the results on data acquired on average 22.5 months afterward demonstrated a possible means to follow disease progression. In particular, the automatically generated IS/OS contour provided a possible objective structural marker for RP progression.

Efficient algorithms for segmenting globally optimal and smooth multi-surfaces

Despite extensive studies in the past, the problem of segmenting globally optimal single and multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method with bi-criteria of global optimality and smoothness for both single and multiple surfaces. Our approach is based on a volumetric graph representation of the 3D image that incorporates curvature information. To evaluate the convergence and performance of our method, we test it on a set of 14 3D OCT images. Our experiments suggest that the proposed method yields optimal (or almost optimal) solutions in 3 to 5 iterations. To the best of our knowledge, this is the first algorithm that utilizes curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. Comparing to the best existing approaches, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images.

Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class support vector machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC>0.93).

Reliability of a computer-aided manual procedure for segmenting optical coherence tomography scans

PURPOSE

To assess the within- and between-operator agreement of a computer-aided manual segmentation procedure for frequency-domain optical coherence tomography scans.

METHODS

Four individuals (segmenters) used a computer-aided manual procedure to mark the borders defining the layers analyzed in glaucoma studies. After training, they segmented two sets of scans, an Assessment Set and a Test Set. Each set had scans from 10 patients with glaucoma and 10 healthy controls. Based on an analysis of the Assessment Set, a set of guidelines was written. The Test Set was segmented twice with a ≥1 month separation. Various measures were used to compare test and retest (within-segmenter) variability and between-segmenter variability including concordance correlations between layer borders and the mean across scans (n = 20) of the mean of absolute differences between local border locations of individual scans, MEAN{mean( ΔLBL )}.

RESULTS

Within-segmenter reliability was good. The mean concordance correlations values for an individual segmenter and a particular border ranged from 0.999 ± 0.000 to 0.978 ± 0.084. The MEAN{mean( ΔLBL )} values ranged from 1.6 to 4.7 μm depending on border and segmenter. Similarly, between-segmenter agreement was good. The mean concordance correlations values for an individual segmenter and a particular border ranged from 0.999 ± 0.001 to 0.992 ± 0.023. The MEAN{mean( ΔLBL )} values ranged from 1.9 to 4.0 μm depending on border and segmenter. The signed and unsigned average positions were considerably smaller than the MEAN{mean( ΔLBL )} values for both within- and between-segmenter comparisons. Measures of within-segmenter variability were only slightly larger than those of between-segmenter variability.

CONCLUSIONS

When human segmenters are trained, the within-and between-segmenter reliability of manual border segmentation is quite good. When expressed as a percentage of retinal layer thickness, the results suggest that manual segmentation provides a reliable measure of the thickness of layers typically measured in studies of glaucoma.

Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach

Optical coherence tomography (OCT) is a noninvasive, depth-resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present a semi-automated segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for the OCT images, which suffer from low contrast and are highly corrupted by noise. A multiphase framework with a circular shape prior is adopted in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on OCT images of rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy even in the presence of intensity inhomogeneity resulting from blood vessels. Our algorithm achieved an average Dice similarity coefficient of 0.84 over all segmented retinal layers, and of 0.94 for the combined nerve fiber layer, ganglion cell layer, and inner plexiform layer which are the critical layers for glaucomatous degeneration.

Model based 3D segmentation and OCT image undistortion of percutaneous implants

Optical Coherence Tomography (OCT) is a noninvasive imaging technique which is used here for in vivo biocompatibility studies of percutaneous implants. A prerequisite for a morphometric analysis of the OCT images is the correction of optical distortions caused by the index of refraction in the tissue. We propose a fully automatic approach for 3D segmentation of percutaneous implants using Markov random fields. Refraction correction is done by using the subcutaneous implant base as a prior for model based estimation of the refractive index using a generalized Hough transform. Experiments show the competitiveness of our algorithm towards manual segmentations done by experts.

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Spectral-domain optical coherence tomography (SD-OCT) provides volumetric images of retinal structures with unprecedented detail. Accurate segmentation algorithms and feature quantification in these images, however, are needed to realize the full potential of SD-OCT. The fully automated segmentation algorithm, FloatingCanvas, serves this purpose and performs a volumetric segmentation of retinal tissue layers in three-dimensional image volume acquired around the optic nerve head without requiring any pre-processing. The reconstructed layers are analyzed to extract features such as blood vessels and retinal nerve fibre layer thickness. Findings from images obtained with the RTVue-100 SD-OCT (Optovue, Fremont, CA, USA) indicate that FloatingCanvas is computationally efficient and is robust to the noise and low contrast in the images. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. The retinal nerve fibre layer thickness maps obtained with this method are clinically realistic and highly reproducible compared with time-domain StratusOCT(TM).

Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid with local binary patterns

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local descriptors are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture information from OCT images of the retina. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. We conducted extensive experiments on a large dataset consisting of 326 OCT scans from 136 patients. The results show that the proposed method is very effective.

Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients

Automated measurements of the retinal nerve fiber layer thickness on circular OCT B-Scans provide physicians additional parameters for glaucoma diagnosis. We propose a novel retinal nerve fiber layer segmentation algorithm for frequency domain data that can be applied on scans from both normal healthy subjects, as well as glaucoma patients, using the same set of parameters. In addition, the algorithm remains almost unaffected by image quality. The main part of the segmentation process is based on the minimization of an energy function consisting of gradient and local smoothing terms. A quantitative evaluation comparing the automated segmentation results to manually corrected segmentations from three reviewers is performed. A total of 72 scans from glaucoma patients and 132 scans from normal subjects, all from different persons, composed the database for the evaluation of the segmentation algorithm. A mean absolute error per A-Scan of 2.9 µm was achieved on glaucomatous eyes, and 3.6 µm on healthy eyes. The mean absolute segmentation error over all A-Scans lies below 10 µm on 95.1% of the images. Thus our approach provides a reliable tool for extracting diagnostic relevant parameters from OCT B-Scans for glaucoma diagnosis.

Longitudinal study of retinal degeneration in a rat using spectral domain optical coherence tomography

Rodent models of retinal degenerative diseases are used by vision scientists to develop therapies and to understand mechanisms of disease progression. Measurement of changes to the thickness of the various retinal layers provides an objective metric to evaluate the performance of the therapy. Because invasive histology is terminal and provides only a single data point, non-invasive imaging modalities are required to better study progression, and to reduce the number of animals used in research. Optical Coherence Tomography (OCT) has emerged as a dominant imaging modality for human ophthalmic imaging, but has only recently gained significant attention for rodent retinal imaging. OCT provides cross section images of retina with micron-scale resolution which permits measurement of the retinal layer thickness. However, in order to be useful to vision scientists, a significant fraction of the retinal surface needs to be measured. In addition, because the retinal thickness normally varies as a function of distance from optic nerve head, it is critical to sample all regions of the retina in a systematic fashion. We present a longitudinal study of OCT to measure retinal degeneration in rats which have undergone optic nerve axotomy, a well characterized form of rapid retinal degeneration. Volumetric images of the retina acquired with OCT in a time course study were segmented in 2D using a semi-automatic segmentation algorithm. Then, using a 3D algorithm, thickness measurements were quantified across the surface of the retina for all volume segmentations. The resulting maps of the changes to retinal thickness over time represent the progression of degeneration across the surface of the retina during injury. The computational tools complement OCT retinal volumetric acquisition, resulting in a powerful tool for vision scientists working with rodents.

Automated layer segmentation of macular OCT images using dual-scale gradient information

A novel automated boundary segmentation algorithm is proposed for fast and reliable quantification of nine intra-retinal boundaries in optical coherence tomography (OCT) images. The algorithm employs a two-step segmentation schema based on gradient information in dual scales, utilizing local and complementary global gradient information simultaneously. A shortest path search is applied to optimize the edge selection. The segmentation algorithm was validated with independent manual segmentation and a reproducibility study. It demonstrates high accuracy and reproducibility in segmenting normal 3D OCT volumes. The execution time is about 16 seconds per volume (480x512x128 voxels). The algorithm shows potential for quantifying images from diseased retinas as well.

Association of visual function and ganglion cell layer thickness in patients with diabetes mellitus type 1 and no or minimal diabetic retinopathy

Diabetic retinopathy (DR) classically presents with micro-aneurysms, small haemorrhages and/or lipoprotein exudates. Several studies have indicated that neural loss occurs in DR even before vascular damage can be observed. This study evaluated the possible relationship between structure (spectral domain-optical coherence tomography) and function (Rarebit visual field test) in patients with type 1 diabetes mellitus and no or minimal diabetic retinopathy (DR). Results demonstrated loss of macular visual function and corresponding thinning of the ganglion cell layer (GCL) in the pericentral area of the macula of diabetic patients (Rs = 0.65, p < 0.001). In multivariable logistic regression analysis, GCL thickness remained an independent predictor of decreased visual function (OR 1.5, 95% CI 1.1-2.1). Early DR seems to include a neurodegenerative component.

Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis

A novel statistical model based on texture and shape for fully automatic intraretinal layer segmentation of normal retinal tomograms obtained by a commercial 800nm optical coherence tomography (OCT) system is developed. While existing algorithms often fail dramatically due to strong speckle noise, non-optimal imaging conditions, shadows and other artefacts, the novel algorithm's accuracy only slowly deteriorates when progressively increasing segmentation task difficulty. Evaluation against a large set of manual segmentations shows unprecedented robustness, even in the presence of additional strong speckle noise, with dynamic range tested down to 12dB, enabling segmentation of almost all intraretinal layers in cases previously inaccessible to the existing algorithms. For the first time, an error measure is computed from a large, representative manually segmented data set (466 B-scans from 17 eyes, segmented twice by different operators) and compared to the automatic segmentation with a difference of only 2.6% against the inter-observer variability.

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 microm and 26.71 microm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 microm and 0.6 and 1.76 microm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R(2)>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Combined image-processing algorithms for improved optical coherence tomography of prostate nerves

Cavernous nerves course along the surface of the prostate gland and are responsible for erectile function. These nerves are at risk of injury during surgical removal of a cancerous prostate gland. In this work, a combination of segmentation, denoising, and edge detection algorithms are applied to time-domain optical coherence tomography (OCT) images of rat prostate to improve identification of cavernous nerves. First, OCT images of the prostate are segmented to differentiate the cavernous nerves from the prostate gland. Then, a locally adaptive denoising algorithm using a dual-tree complex wavelet transform is applied to reduce speckle noise. Finally, edge detection is used to provide deeper imaging of the prostate gland. Combined application of these three algorithms results in improved signal-to-noise ratio, imaging depth, and automatic identification of the cavernous nerves, which may be of direct benefit for use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Automated layer segmentation of optical coherence tomography images

Under the framework of computer-aided diagnosis, optical coherence tomography (OCT) has become an established ocular imaging technique that can be used in glaucoma diagnosis by measuring the retinal nerve fiber layer thickness. This letter presents an automated retinal layer segmentation technique for OCT images. In the proposed technique, an OCT image is first cut into multiple vessel and nonvessel sections by the retinal blood vessels that are detected through an iterative polynomial smoothing procedure. The nonvessel sections are then filtered by a bilateral filter and a median filter that suppress the local image noise but keep the global image variation across the retinal layer boundary. Finally, the layer boundaries of the filtered nonvessel sections are detected, which are further classified to different retinal layers to determine the complete retinal layer boundaries. Experiments over OCT for four subjects show that the proposed technique segments an OCT image into five layers accurately.

Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula

Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions--SEADs (Symptomatic Exudate-Associated Derangements)--in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of 0.961 +/- 0.012 was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and--together with an anatomical atlas of normal retinas--can be used for clinically important applications.

Automated quantification of inherited phenotypes from color images: a twin study of the variability of optic nerve head shape

PURPOSE

Discovery and description of heritable optic nerve head (ONH) phenotypes have been haphazard. In this preliminary study, the authors test the hypothesis that inheritable phenotypes can be discovered and quantified computationally by estimating three-dimensional ONH shape parameters from stereo color photographs from the Twins Eye Study in Tasmania and determining how much of the variability in ONH shape is accounted for by genetic influence.

METHODS

Three-dimensional ONH shape was estimated by an automated algorithm from stereoscopic optic disc color photographs of a random sample of 172 subjects (344 eyes, 45 pairs of monozygotic [MZ] and 41 dizygotic [DZ] twins). Shape resemblances between eyes were quantified with a distance metric. The heritability of the shape resemblance was determined both through the distribution of the discongruence indices and through structural equation modeling techniques (ACE model).

RESULTS

Significantly different discongruence indices were found for MZ (1.0286; 95% CI, 0.9872-1.0701) and DZ twins (1.4218; 95% CI, 1.2631-1.5804); larger indices for DZ twins indicated that variability was substantially determined by genetic factors. The standardized variances of the A(dditive genetic), C(ommon environmental), and (nonshared) E(nvironmental) components were 0.80, 2.00 × 10(-15) and 0.20, respectively, for all OD, and 0.79, 3.24 × 10(-14), and 0.21 for all OS.

CONCLUSIONS

This preliminary study shows that quantitative phenotyping of the ONH shape from color images leads to phenotypes that can be measured and are largely under genetic control. The association of these inherited phenotypes with genotypes deserves confirmation and further study.

Retinal imaging and image analysis

Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

Retinal imaging and image analysis

Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

Segmentation of the optic disc in 3-D OCT scans of the optic nerve head

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

Intra-retinal layer segmentation in optical coherence tomography images

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

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

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of 5.69+/-2.41 microm was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of 5.71+/-1.98 microm.

Measurement of local retinal ganglion cell layer thickness in patients with glaucoma using frequency-domain optical coherence tomography

OBJECTIVE

To explore the feasibility of obtaining a local measurement of the thickness of the retinal ganglion cell layer in patients with glaucoma using frequency-domain optical coherence tomography (fdOCT) and a computer-aided manual segmentation procedure.

METHODS

The fdOCT scans were obtained from the horizontal midline for 1 eye of 26 patients with glaucoma and 20 control subjects. The thickness of various layers was measured with a manual segmentation procedure aided by a computer program. The patients were divided into low- and high-sensitivity groups based on their foveal sensitivity on standard automated perimetry.

RESULTS

The RGC plus inner plexiform and the retinal nerve fiber layers of the low-sensitivity group were significantly thinner than those of the high-sensitivity group. While these layers were thinner in the patients than the controls, the thicknesses of inner nuclear layer and receptor layer were similar in all 3 groups. Further, the thinning of the retinal ganglion cell plus inner plexiform layer in 1 glaucoma-affected eye showed qualitative correspondence to the loss in 10-2 visual field sensitivity.

CONCLUSIONS

Local measures of RGC layer thickness can be obtained from fdOCT scans using a manual segmentation procedure, and these measures show qualitative agreement with visual field sensitivity.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Automated segmentation of tissue structures in optical coherence tomography data

Segmentation of optical coherence tomography (OCT) images provides useful information, especially in medical imaging applications. Because OCT images are subject to speckle noise, the identification of structures is complicated. Addressing this issue, two methods for the automated segmentation of arbitrary structures in OCT images are proposed. The methods perform a seeded region growing, applying a model-based analysis of OCT A-scans for the seed's acquisition. The segmentation therefore avoids any user-intervention dependency. The first region-growing algorithm uses an adaptive neighborhood homogeneity criterion based on a model of an OCT intensity course in tissue and a model of speckle noise corruption. It can be applied to an unfiltered OCT image. The second performs region growing on a filtered OCT image applying the local median as a measure for homogeneity in the region. Performance is compared through the quantitative evaluation of artificial data, showing the capabilities of both in terms of structures detected and leakage. The proposed methods were tested on real OCT data in different scenarios and showed promising results for their application in OCT imaging.

Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy

PURPOSE

To determine whether type 1 diabetes preferentially affects the inner retinal layers by comparing the thickness of six retinal layers in type 1 diabetic patients who have no or minimal diabetic retinopathy (DR) with those of age- and sex-matched healthy controls.

METHODS

Fifty-seven patients with type 1 diabetes with no (n = 32) or minimal (n = 25) DR underwent full ophthalmic examination, stereoscopic fundus photography, and optical coherence tomography (OCT). After automated segmentation of intraretinal layers of the OCT images, mean thickness was calculated for six layers of the retina in the fovea, the pericentral area, and the peripheral area of the central macula and were compared with those of an age- and sex-matched control group.

RESULTS

In patients with minimal DR, the mean ganglion cell/inner plexiform layer was 2.7 microm thinner (95% confidence interval [CI], 2.1-4.3 microm) and the mean inner nuclear layer was 1.1 microm thinner (95% CI, 0.1-2.1 microm) in the pericentral area of the central macula compared to those of age-matched controls. In the peripheral area, the mean ganglion cell/inner plexiform layer remained significantly thinner. No other layers showed a significant difference.

CONCLUSIONS

Thinning of the total retina in type 1 diabetic patients with minimal retinopathy compared with healthy controls is attributed to a selective thinning of inner retinal layers and supports the concept that early DR includes a neurodegenerative component.