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Biomed Opt Express. 2013 Jan 1;4(1):134-50. doi: 10.1364/BOE.4.000134. Epub 2012 Dec 17.

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

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  • 1Center for Medical Physics and Biomedical Engineering, Medical University Vienna, General Hospital Vienna 4L, Waehringer Guertel 18-20, A-1090 Vienna, Austria.


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.


(100.0100) Image processing; (100.3008) Image recognition, algorithms and filters; (170.4500) Optical coherence tomography; (170.4580) Optical diagnostics for medicine

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