Age-Related Macular Degeneration (AMD) is a common eye disease characterized by the build-up of drusen, small deposits of extracellular materials in the macula. Early detection of drusen is key to understanding the progression of AMD. Therefore, accurate and robust segmentation of drusen during AMD progression is important for automated detection, classification, diagnosis, and prognosis tasks. Spectral-domain optical coherence tomography (SD-OCT) is a popular macular imaging modality used for these tasks. However, because of the trade-off between resolution and speed, often clinical OCT scans will contain far fewer images per volume than the 100-200 images the drusen segmentation literature generally utilizes. To address this disparity, we develop a novel drusen segmentation algorithm for SD-OCT volumes with low volumetric resolution. We achieve comparable results to similar work, while using on average 16% of the volumetric information. We evaluate our segmentation approach on manually segmented images by two graders, and achieve median Dice coefficient scores of 0.75 and 0.66, respectively, which are close to our median inter-reader variability score of 0.75.
Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in older individuals. Clinically, ophthalmologists visually inspect optical coherence tomography (OCT) volumes to diagnose the stage of AMD based on well-known biomarkers. An early characteristic of AMD is drusen, which appears as yellowish deposits under the retina. AMD is mainly categorized into two types: dry AMD (non-neovascular) and wet AMD (neovascular). Given the large number of OCT images in an individual volume, an efficacious computer-aided detection system can reduce the workload for ophthalmologists by automatically detecting biomarkers in the relevant images. Because the shape of the RPE is critical in defining the pathological changes caused by wet and dry AMD, we propose a novel approach to describe the RPE shape using Mel Frequency Cepstral Coefficients (MFCC). Our previous work indicates that Haralick texture features have the ability to distinguish drusen from healthy tissue on color photography, therefore, we also investigated Haralick texture features extracted from the region between Inner Limiting Membrane (ILM) and Bruchs Membrane (BM) layers in this study. We achieved a mean accuracy, sensitivity with respect to AMD image and specificity with respect to healthy image of 89.68%, 89.26% and 90.12% on testing sets and 69.22%, 67.40%, and 75.56% on new patient validation sets, respectively. Our binary classification results indicate that MFCC are uniquely suited for producing generalizable results to automatically detect AMD biomarker images.
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