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Abstract. Recent developments in Fourier domain-optical coherence tomography ͑Fd-OCT͒ have increased the acquisition speed of current ophthalmic Fd-OCT instruments sufficiently to allow the acquisition of volumetric data sets of human retinas in a clinical setting. The large size and three-dimensional ͑3D͒ nature of these data sets require that intelligent data processing, visualization, and analysis tools are used to take full advantage of the available information. Therefore, we have combined methods from volume visualization, and data analysis in support of better visualization and diagnosis of Fd-OCT retinal volumes. Custom-designed 3D visualization and analysis software is used to view retinal volumes reconstructed from registered B-scans. We use a support vector machine ͑SVM͒ to perform semiautomatic segmentation of retinal layers and structures for subsequent analysis including a comparison of measured layer thicknesses. We have modified the SVM to gracefully handle OCT speckle noise by treating it as a characteristic of the volumetric data. Our software has been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.

Abstract-We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have "global awareness." A feature, such as a retinal layer, can therefore be modeled within the SVM as a combination of statistical characteristics across all levels; thus capturing high-and low-frequency information. We have compared our semi-automatically generated segmentations to manually segmented layers for verification purposes. Our main goals were to provide a tool that could (i) be used in a clinical setting; (ii) operate on noisy OCT data; and (iii) isolate individual or multiple retinal layers in both healthy and disease cases that contain structural deformities.

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The acquisition speed of current FD-OCT (Fourier Domain -Optical Coherence Tomography) instruments allows rapid screening of three-dimensional (3D) volumes of human retinas in clinical settings. To take advantage of this ability requires software used by physicians to be capable of displaying and accessing volumetric data as well as supporting post processing in order to access important quantitative information such as thickness maps and segmented volumes.We describe our clinical FD-OCT system used to acquire 3D data from the human retina over the macula and optic nerve head. B-scans are registered to remove motion artifacts and post-processed with customized 3D visualization and analysis software. Our analysis software includes standard 3D visualization techniques along with a machine learning support vector machine (SVM) algorithm that allows a user to semi-automatically segment different retinal structures and layers. Our program makes possible measurements of the retinal layer thickness as well as volumes of structures of interest, despite the presence of noise and structural deformations associated with retinal pathology. Our software has been tested successfully in clinical settings for its efficacy in assessing 3D retinal structures in healthy as well as diseased cases. Our tool facilitates diagnosis and treatment monitoring of retinal diseases.

An accurate solid eye model (with volumetric retinal morphology) has many applications in the field of ophthalmology, including evaluation of ophthalmic instruments and optometry/ophthalmology training. We present a method that uses volumetric OCT retinal data sets to produce an anatomically correct representation of three-dimensional (3D) retinal layers. This information is exported to a laser scan system to re-create it within solid eye retinal morphology of the eye used in OCT testing. The solid optical model eye is constructed from PMMA acrylic, with equivalent optical power to that of the human eye (~58D). Additionally we tested a water bath eye model from Eyetech Ltd. with a customized retina consisting of five layers of ~60 µm thick biaxial polypropylene film and hot melt rubber adhesive.

We report on the development of quantitative, reproducible diagnostic observables for age-related macular degeneration (AMD) based on high speed spectral domain optical coherence tomography (SDOCT). 3D SDOCT volumetric data sets (512 x 1000 x 100 voxels) were collected (5.7 seconds acquisition time) in over 50 patients with age-related macular degeneration and geographic atrophy using a state-of-the-art SDOCT scanner. Commercial and custom software utilities were used for manual and semi-automated segmentation of photoreceptor layer thickness, total drusen volume, and geographic atrophy cross-sectional area. In a preliminary test of reproducibility in segmentation of total drusen volume and geographic atrophy surface area, inter-observer error was less than 5%. Extracted volume and surface area of AMDrelated drusen and geographic atrophy, respectively, may serve as useful observables for tracking disease state that were not accessible without the rapid 3D volumetric imaging capability unique to retinal SDOCT.