Automated three‐dimensional cell counting method for grading uveitis of rodent eye in vivo with optical coherence tomography

Abstract

In preclinical vision research, cell grading in small animal models is essential for the quantitative evaluation of intraocular inflammation. Here, we present a new and practical optical coherence tomography (OCT) image analysis method for the automated detection and counting of aqueous cells in the anterior chamber (AC) of a rodent model of uveitis. Anterior segment OCT images are acquired with a 100 kHz swept-source OCT system. The proposed method consists of 2 steps. In the first step, we first despeckle and binarize each OCT image. After removing AS structures in the binary image, we then apply area thresholding to isolate cell-like objects. Potential cell candidates are selected based on their best fit to roundness. The second step performs the cell counting within the whole AC, in which additional cell tracking analysis is conducted on the successive OCT images to eliminate redundancy in cell counting. Finally, 3D cell grading using the proposed method is demonstrated in longitudinal OCT imaging of a mouse model of anterior uveitis in vivo. Rendering of anterior segment (orange) of mouse eye and automatically counted anterior chamber cells (green). Inset is a top view of the rendering, showing the cell distribution across the anterior chamber.

Ocular anterior chamber blood cell population differentiation using spectroscopic optical coherence tomography

“…Optical coherence tomography (OCT) allows noninvasive, high-resolution cross-sectional imaging of the AC [9]. Several groups have investigated its potential in grading AC inflammation by locating and counting cells within anterior segment OCT (AS-OCT) images [10][11][12][13][14]. However, quantitative analysis of the true composition of cells using OCT remains a challenge.…”

Tarsier Anterior Chamber Cell Grading: Improving the SUN Grading Scheme with a Visual Analog Scale

“…6,8,9 However, concern over particles sized less than the axial resolution of the OCT that would be missed may prevent its uniform implementation. 8,10 In our cumulative experience, OCT use for this purpose is limited and most devices do not have the necessary resolutions needed to enable a precise cell count. Beyond clinical treatment, this inability to consistently assess the level of anterior chamber cells between different graders poses difficulties in clinical studies where the need to ensure consensus among investigators/graders is of paramount importance.…”

Quantitative Assessment of Experimental Ocular Inflammatory Disease

“…Pooling data from animal cohorts at selected timepoints runs the risk of obscuring subtle patterns, and overweighting the importance of the certain trends. This can be countered by the use of analysis that exploits modern image processing, with its scope for a higher degree of quantitation (66)(67)(68). A critical element of complementary analysis is therefore the use of non-invasive techniques and computational means to maximize information retrieved from the data.…”

“…Importantly, the subjective manual element of the segmentation step was eliminated. The automated segmentation step involved removal of anatomical structures connected to image boundaries (75). Compared with counts from histological sections, OCT tended to undercount, which was attributed to insensitivity to cell clumps, sediments and exclusion of the extremities of the iris interface (74).…”