Automated three‐dimensional cell counting method for grading uveitis of rodent eye in vivo with optical coherence tomography. Issue 9 (11th June 2018)

• Record Type: Journal Article
• Authors: Choi, Woo J.; Pepple, Kathryn L.; Wang, Ruikang K.
• Is Part Of: Journal of biophotonics. Volume 11:Issue 9(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-11
• Subjects: 3D cell counting -- anterior chamber cell -- ocular inflammation -- optical coherence tomography -- uveitis; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201800140
• Languages: English
• ISSNs: 1864-063X

Abstract

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. Abstract : Cell grading in a small animal model is important for evaluation of preclinical ocular inflammation. This paper presents a new image analysis method for automated detection and counting of the anterior chamber cells in optical coherence tomography (OCT) images of a uveitis model of mouse. The proposed cell tracking analysis is performed on the successive OCT images, allowing visualization and quantification of cell distribution in the whole anterior chamber.