Related Experiment Video
Updated: Mar 18, 2026
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
Published on: July 24, 2020
Quantitative Assessment of Anterior Segment Inflammation in a Rat Model of Uveitis Using Spectral-Domain Optical
Kathryn L Pepple1, Woo June Choi2, Leslie Wilson1
1Department of Ophthalmology University of Washington, Seattle, Washington, United States.
Purpose:
To develop anterior segment spectral-domain optical coherence tomography (SD-OCT) and quantitative image analysis for use in experimental uveitis in rats.
Methods:
Acute anterior uveitis was generated in Lewis rats. A spectral domain anterior segment OCT system was used to image the anterior chamber (AC) and ciliary body at baseline and during peak inflammation 2 days later. Customized MatLab image analysis algorithms were developed to segment the AC, count AC cells, calculate central corneal thickness (CCT), segment the ciliary body and zonules, and quantify the level of ciliary body inflammation with the ciliary body index (CBI). Images obtained at baseline and during peak inflammation were compared. Finally, longitudinal imaging and image analysis was performed over the 2-week course of inflammation.
Results:
Spectral-domain optical coherence tomography identifies structural features of inflammation. Anterior chamber cell counts at peak inflammation obtained by automated image analysis and human grading were highly correlated (r = 0.961), and correlated well with the histologic score of inflammation (r = 0.895). Inflamed eyes showed a significant increase in average CCT (27 μm, P = 0.02) and an increase in average CBI (P < 0.0001). Longitudinal imaging and quantitative image analysis identified a significant change in AC cell and CBI on day 2 with spontaneous resolution of inflammation by day 14.
Conclusions:
Spectral-domain optical coherence tomography provides high-resolution images of the structural changes associated with anterior uveitis in rats. Anterior chamber cell count and CBI determined by semi-automated image analysis strongly correlates with inflammation, and can be used to quantify inflammation longitudinally in single animals.
