Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

A fully automated, robust vessel segmentation algorithm has been developed for choroidal OCT, employing multiscale 3D edge filtering and projection of “probability cones” to determine the vessel “core”, even in the tomograms with low signal-to-noise ratio (SNR). Based on the ideal vessel response after registration and multiscale filtering, with computed depth related SNR, the vessel core estimate is dilated to quantify the full vessel diameter. As a consequence, various statistics can be computed using the 3D choroidal vessel information, such as ratios of inner (smaller) to outer (larger) choroidal vessels or the absolute/relative volume of choroid vessels. Choroidal vessel quantification can be displayed in various forms, focused and averaged within a special region of interest, or analyzed as the function of image depth. In this way, the proposed algorithm enables unique visualization of choroidal watershed zones, as well as the vessel size reduction when investigating the choroid from the sclera towards the retinal pigment epithelium (RPE). To the best of our knowledge, this is the first time that an automatic choroidal vessel segmentation algorithm is successfully applied to 1060 nm 3D OCT of healthy and diseased eyes.OCIS codes: (170.4500) Optical coherence tomography, (100.0100) Image processing, (100.3008) Image recognition, algorithms and filters, (170.4580) Optical diagnostics for medicine     

Tear sample collection using cellulose acetate absorbent filters

Purpose: To assess the use of cellulose acetate filter rods as a technique for tear collection. Method: The cellulose acetate rod (CR) was compared with the ‘standard’ glass capillary tube (CT), in a series of experiments, to assess: sample collection by collected volume size; the effect of tear stimulation on total tear protein concentration and major tear protein concentrations; and technique invasiveness. Results: No difference was found in concentrations for total protein, IgA (secretory immunoglobulin A), lactoferrin and lysozyme (p > 0.05) with no clinically significant increase in serum albumin to show serum leakage. Sample volume was higher for CR (p < 0.005) and sample volume increased for stimulated collection with CR (p = 0.001). Dilution effect of a stimulated sample size was reliably shown only with CR (r = ?0.66, p = 0.011). Using bovine albumin standard with CR and CT, a smaller sample volume (p < 0.001) and a higher protein concentration (p < 0.001) were extracted with CR. Conclusion: The cellulose rod offers a suitable alternative to the glass CT. It is able to quickly absorb a sample, allowing use for a wide range of sample sizes, while being minimally invasive.     

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.