Volumetric quantification of choroid and Haller's sublayer using OCT scans: An accurate and unified approach based on...

The choroid, a dense vascular structure in the posterior segment of the eye, maintains the health of the retina by supplying oxygen and nutrients, and assumes clinical significance in screening ocular diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR). As a technological assist, algorithmic estimation of choroidal biomarkers has been suggested based on sectional (B-scan) optical coherence tomography (OCT) images. However, most such 2D estimation techniques are compute-intensive, yet enjoy limited accuracy and have only been validated on OCT image datasets of healthy eyes. Not surprisingly, fine-scale analyses, including those involving Haller's sublayer, remain relatively rare and unsophisticated. Against this backdrop, we propose an efficient algorithm to quantify desired biomarkers with improved accuracy based on volume OCT scans. Specifically, we attempted an accurate, computationally light volumetric segmentation method involving stratified smoothing to detect choroid and Haller's sublayer.

For detecting the various boundaries of the choroid and the Haller's sublayer, we propose a common volumetric method that performs suitable exponential enhancement and maintains smooth spatial continuity across 2D B-scans. Further, we achieve suitable volumetric smoothing by primarily deploying light-duty linear regression, and sparingly using compute-intensive tensor voting, and hence significantly reduce overall complexity. The proposed methodology is tested on five health and five diseased OCT volumes considering various metrics including volumetric Dice coefficient and corresponding quotient measures to facilitate comparison vis-à-vis intra-observer repeatability.

On five healthy and five diseased OCT volumes, respectively, the proposed method for choroid segmentation recorded volumetric Dice coefficients of 93.53 % and 93.30 %, which closely approximate the respective reference observer repeatability values of 95.60 % and 95.49 %. In terms of related quotient measures, our method achieved more than 50 % improvement over a recently reported method. In detecting Haller's sublayer as well, our algorithm records statistical performance closely matching that of reference manual method.

Advancing the state-of-the-art, the proposed volumetric segmentation, tested on both healthy and diseased datasets, demonstrated close match with the manual reference. Our method assumes significance in accurate screening of chorioretinal diseases including AMD, CSCR and pachychoroid. Further, it enables generating accurate training data for developing deep learning models for improved detection of choroid and Haller's sublayer.