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The choroid is the vascular coat of the eye, and its role has been studied in multiple chorioretinal disorders. Recent advancements in choroidal imaging techniques, including enhanced depth imaging optical coherence tomography, swept source optical coherence tomography, en face optical coherence tomography, and optical coherence tomography angiography have facilitated an in-depth analysis of the choroid. The gradual shift from manual to automated segmentation and binarization methods have led to precise and reproducible measurements of choroidal parameters. These qualitative and quantitative parameters, called choroidal imaging biomarkers, have evolved over the past decade from a simple linear subfoveal choroidal thickness to more complex 3D choroidal reconstruction, thus widening the spectrum encompassing multiple parameters. These biomarkers have provided a better understanding of the pathogenesis, are helpful in diagnostic dilemmas, and, in the future may also help to devise treatment options. The lack of normative data, absence of standardized parameters, and limitations of the imaging techniques, however, have led to ambiguity and difficulty in the interpretation of these variables. We attempt to address these lacunae in the literature and provide a basic understanding of the choroid in both health and disease using these choroidal biomarkers. [ABSTRACT FROM AUTHOR]

A variety of vision ailments are indicated by anomalies in the choroid layer of the posterior visual section. Consequently, choroidal thickness and volume measurements, usually performed by experts based on optical coherence tomography (OCT) images, have assumed diagnostic significance. Now, to save precious expert time, it has become imperative to develop automated methods. To this end, one requires choroid outer boundary (COB) detection as a crucial step, where difficulty arises as the COB divides the choroidal granularity and the scleral uniformity only notionally, without marked brightness variation. In this backdrop, we measure the structural dissimilarity between choroid and sclera by structural similarity (SSIM) index, and hence estimate the COB by thresholding. Subsequently, smooth COB estimates, mimicking manual delineation, are obtained using tensor voting. On five datasets, each consisting of 97 adult OCT B-scans, automated and manual segmentation results agree visually. We also demonstrate close statistical match (greater than 99.6% correlation) between choroidal thickness distributions obtained algorithmically and manually. Further, quantitative superiority of our method is established over existing results by respective factors of 27.67% and 76.04% in two quotient measures defined relative to observer repeatability. Finally, automated choroidal volume estimation, being attempted for the first time, also yields results in close agreement with that of manual methods. [ABSTRACT FROM AUTHOR]