PurposeTo achieve reproducible imaging of the choriocapillaris and associated flow voids using swept-source OCT angiography (SS-OCTA).MethodsSubjects were enrolled and SS-OCTA was performed using the 3 × 3 mm scan pattern. Blood flow was identified using the complex optical microangiography (OMAG) algorithm. The choriocapillaris was defined as a slab from the outer boundary of Bruch's membrane (BM) to approximately 20 μm below BM. Compensation for the shadowing effect caused by the RPE and BM complex on the choriocapillaris angiogram was achieved by using the structural information from the same slab. A thresholding method to calculate the percentage of flow voids from a region was developed based on a normal database.ResultsTwenty normal subjects and 12 subjects with drusen were enrolled. SS-OCTA identified the choriocapillaris in normal subjects as a lobular plexus of capillaries in the central macula and the lobular arrangement became more evident toward the periphery. In all eyes, signal compensation resulted in fewer choriocapillaris flow voids with improved repeatability of measurements. The best repeatability for the measurement was achieved by using 1 standard deviation (SD) for the thresholding strategy.ConclusionsSS-OCTA can image the choriocapillaris in vivo, and the repeatability of flow void measurements is high in the presence of drusen. The ability to image the choriocapillaris and associated flow voids should prove useful in understanding disease onset, progression, and response to therapies.

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Our results demonstrate the potential ability of our model to identify those AMD patients at risk of progressing to exudative AMD from an early or intermediate stage.

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Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method can accurately extract the object boundary while guaranteeing certain noise robustness. Furthermore, the proposed algorithm completely avoids the pre-processing steps typical of region-based contour model segmentation, resulting in a higher preservation of image details. Experiments performed on synthetic images and real word images demonstrate that the proposed algorithm, as compared with the state-of-art algorithms, is more efficient and robust to higher noise level manifestations in the images.