Luis de Sisternes

Quantitative SD-OCT Imaging Biomarkers as Indicators of Age-Related Macular Degeneration Progression

PURPOSE We developed a statistical model based on quantitative characteristics of drusen to estimate the likelihood of conversion from early and intermediate age-related macular degeneration (AMD) to its advanced exudative form (AMD progression) in the short term (less than 5 years), a crucial task to enable early intervention and improve outcomes. METHODS Image features of drusen quantifying their number, morphology, and reflectivity properties, as well as the longitudinal evolution in these characteristics, were automatically extracted from 2146 spectral-domain optical coherence tomography (SD-OCT) scans of 330 AMD eyes in 244 patients collected over a period of 5 years, with 36 eyes showing progression during clinical follow-up. We developed and evaluated a statistical model to predict the likelihood of progression at predetermined times using clinical and image features as predictors. RESULTS Area, volume, height, and reflectivity of drusen were informative features distinguishing between progressing and nonprogressing cases. Discerning progression at follow-up (mean, 6.16 months) resulted in a mean area under the receiver operating characteristic curve (AUC) of 0.74 (95% confidence interval [CI], 0.58, 0.85). The maximum predictive performance was observed at 11 months after a patients first early AMD diagnosis, with mean AUC 0.92 (95% CI, 0.83, 0.98). Those eyes predicted to progress showed a much higher progression rate than those predicted not to progress at any given time from the initial visit. CONCLUSIONS Our results demonstrate the potential ability of our model to identify those AMD patients at risk of progressing to exudative AMD from an early or intermediate stage.

Automated drusen segmentation and quantification in SD-OCT images

Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome.

Robust noise region-based active contour model via local similarity factor for image segmentation

Abstract Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method can accurately extract the object boundary while guaranteeing certain noise robustness. Furthermore, the proposed algorithm completely avoids the pre-processing steps typical of region-based contour model segmentation, resulting in a higher preservation of image details. Experiments performed on synthetic images and real word images demonstrate that the proposed algorithm, as compared with the state-of-art algorithms, is more efficient and robust to higher noise level manifestations in the images.

Localization of Damage in Progressive Hydroxychloroquine Retinopathy On and Off the Drug: Inner Versus Outer Retina, Parafovea Versus Peripheral Fovea

PURPOSE To evaluate the relative involvement of inner and outer retina in hydroxychloroquine (HCQ) retinopathy while on the drug, and after drug cessation, using data from spectral-domain optical coherence tomography (SD-OCT). METHODS A total of 102 SD-OCT scans were obtained from 11 patients (classified as having early, moderate, or severe stages of toxicity) over a period of 4 years after cessation of HCQ. The inner and outer retina boundaries were identified automatically to measure thickness and characterize progression topographically. RESULTS The segmentation of retinal layers was verified in SD-OCT cross-sections for all eyes and scans included in this study (a total of 102 scans). Topographic analysis showed that inner retina was not involved in HCQ toxicity to any meaningful degree, either between stages of retinopathy or after the drug is stopped. The characteristic bulls eye pattern of outer macula thinning appears when comparing moderate retinopathy (before any RPE damage) to the early stage. Later damage, as toxicity evolved to a severe stage, was diffuse across most of the macula. If the drug was stopped at an early or moderate stage, progression was limited to the first year and occurred diffusely without parafoveal localization. CONCLUSIONS Hydroxychloroquine retinopathy primarily involves outer retina (photoreceptors). Outer retinal thinning while using HCQ initially involves the parafovea, but becomes diffuse across the macula as damage progresses or after drug cessation. When HCQ is stopped at an early or moderate stage (before RPE damage), progression seems to be limited to the first year.

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.

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.

Fully Automated Prediction of Geographic Atrophy Growth Using Quantitative Spectral-Domain Optical Coherence Tomography Biomarkers

PURPOSE To develop a predictive model based on quantitative characteristics of geographic atrophy (GA) to estimate future potential regions of GA growth. DESIGN Progression study and predictive model. PARTICIPANTS One hundred eighteen spectral-domain (SD) optical coherence tomography (OCT) scans of 38 eyes in 29 patients. METHODS Imaging features of GA quantifying its extent and location, as well as characteristics at each topographic location related to individual retinal layer thickness and reflectivity, the presence of pathologic features (like reticular pseudodrusen or loss of photoreceptors), and other known risk factors of GA growth, were extracted automatically from 118 SD OCT scans of 38 eyes from 29 patients collected over a median follow-up of 2.25 years. We developed and evaluated a model to predict the magnitude and location of GA growth at given future times using the quantitative features as predictors in 3 possible scenarios. MAIN OUTCOME MEASURES Potential regions of GA growth. RESULTS In descending order of out-of-bag feature importance, the most predictive SD OCT biomarkers for predicting the future regions of GA growth were thickness loss of bands 11 through 14 (5.66), reflectivity of bands 11 and 12 (5.37), thickness of reticular pseudodrusen (5.01), thickness of bands 5 through 11 (4.82), reflectivity of bands 7 through 11 (4.78), GA projection image (4.73), increased minimum retinal intensity map (4.59), and GA eccentricity (4.49). The predicted GA regions in the 3 tested scenarios resulted in a Dice index mean ± standard deviation of 0.81±0.12, 0.84±0.10, and 0.87±0.06, respectively, when compared with the observed ground truth. Considering only the regions without evidence of GA at baseline, predicted regions of future GA growth showed relatively high Dice indices of 0.72±0.18, 0.74±0.17, and 0.72±0.22, respectively. Predictions and actual values of GA growth rate and future GA involvement in the central fovea showed high correlations. CONCLUSIONS Experimental results demonstrated the potential of our predictive model to predict future regions where GA is likely to grow and to identify the most discriminant early indicator (thickness loss of bands 11 through 14) of regions susceptible to GA growth.

Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor.

Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. 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 quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

An improved optical coherence tomography-derived fundus projection image for drusen visualization.

Purpose: To develop and evaluate an improved method of generating en face fundus images from three-dimensional optical coherence tomography images which enhances the visualization of drusen. Methods: We describe a novel approach, the restricted summed-voxel projection (RSVP), to generate en face projection images of the retinal surface combined with an image processing method to enhance drusen visualization. The RSVP approach is an automated method that restricts the projection to the retinal pigment epithelium layer neighborhood. Additionally, drusen visualization is improved through an image processing technique that fills drusen with bright pixels. The choroid layer is also excluded when creating the RSVP to eliminate bright pixels beneath drusen that could be confused with drusen when geographic atrophy is present. The RSVP method was evaluated in 46 patients and 3-dimensional optical coherence tomography data sets were obtained from 8 patients, for which 2 readers independently identified drusen as the gold standard. The mean drusen overlap ratio was used as the metric to determine the accuracy of visualization of the RSVP method when compared with the conventional summed-voxel projection technique. Results: Comparative results demonstrate that the RSVP method was more effective than the conventional summed-voxel projection in displaying drusen and retinal vessels, and was more useful in detecting drusen. The mean drusen overlap ratios based on the conventional summed-voxel projection method and the RSVP method were 2.1% and 89.3%, respectively. Conclusion: The RSVP method was more effective for drusen visualization than the conventional summed-voxel projection method, and it may be useful for macular assessment in patients with nonexudative age-related macular degeneration.

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

PURPOSE To determine the value of topographic spectral-domain optical coherence tomography (SD-OCT) imaging features assessed after macular hole repair surgery in predicting visual acuity (VA) outcomes. METHODS An automated algorithm was developed to topographically outline and quantify area, extent, and location of defects in the ellipsoid zone (EZ) band and inner retina layers in SD-OCT scans. We analyzed the correlation of these values with VA in longitudinal observations from 35 patients who underwent successful macular hole surgery, in their first observation after surgery (within 2 months), and in a single observation within 6 to 12 months after surgery. Image features assessed at the first visit after surgery were also investigated as possible predictors of future VA improvement. RESULTS Significant correlation with longitudinal VA was found for the extent, circularity, and ratio of defects in EZ band at the fovea and parafoveal regions. The ratio of defects in EZ band at the fovea, temporal-inner, and inferior-inner macula regions showed significant strong correlation with VA within 6 to 12 months post surgery. Patients with worse vision outcome at such time also had a significantly higher rate of inner retinal defects in the superior-outer region in their first postsurgery observation. CONCLUSIONS A lowering extent of EZ band defects in the foveal and parafoveal regions is a good indicator of postsurgery VA recovery. Attention should also be given to postsurgical alterations in the inner retina, as patients with more extensive atrophic changes appear to have slower or worse VA recovery despite closure of the macular hole.