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.

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 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.