Department of Bioengineering, University of Washington, Seattle, Washington.
School of Electrical and Electronics Engineering, College of ICT Engineering, Chung-Ang University, Seoul, Republic of Korea.
J Biophotonics. 2018 Sep;11(9):e201800140. doi: 10.1002/jbio.201800140. Epub 2018 Jun 11.
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.
在临床前视觉研究中，对小动物模型中的细胞进行分级对于评估眼内炎症的定量评估至关重要。在这里，我们提出了一种新的实用光学相干断层扫描（OCT）图像分析方法，用于自动检测和计数葡萄膜炎啮齿动物模型前房（AC）中的房水细胞。使用 100 kHz 扫频源 OCT 系统获取眼前节 OCT 图像。该方法包括 2 个步骤。在第一步中，我们首先对每个 OCT 图像进行去噪和二值化。在去除二进制图像中的 AS 结构后，我们应用面积阈值将细胞状物体分离出来。根据其与圆形的最佳拟合度选择潜在的细胞候选物。第二步在整个 AC 中进行细胞计数，在该步骤中，对连续的 OCT 图像进行额外的细胞跟踪分析，以消除细胞计数中的冗余。最后，使用提出的方法在体内急性前葡萄膜炎的小鼠模型的纵向 OCT 成像中进行了 3D 细胞分级。显示了鼠标眼睛前段（橙色）和自动计数的前房细胞（绿色）的渲染。插图是渲染的顶视图，显示了前房内的细胞分布。
