Wu Hao, Qiao Zhongbao, Cheng Chi, Luo Wenting, Wan Ting, Lu Na, Qiao Tong, Di Yue
School of Medicine, Department of Ophthalmology, Shanghai Children' s Hospital, Shanghai Jiao Tong University city, Shanghai, 200062, China.
Department of Ophthalmology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Sci Rep. 2025 Aug 17;15(1):30049. doi: 10.1038/s41598-025-13096-y.
To develop a Python-based digital technique for accurate measurement of pupil size, corneal size, and eccentricity in guinea pigs, and to validate its efficiency and accuracy against traditional OCT methods in ophthalmology research.
Fourteen healthy guinea pigs were selected, and the eye images were captured using a camera, and the image analysis program was written by Python 3.9. The program integrated edge detection (Canny algorithm), curve fitting (conic curve equation) and pixel-actual distance conversion modules, and designed a graphical user interface (GUI) to visualize the operation. Measured parameters included pupil size, corneal size and eccentricity to compare the difference between in vivo and ex vivo measurements and to verify the accuracy of the method.
The Python program clearly identified the guinea pig pupil and corneal limbus, and the fitted curves were in high agreement with the actual contours. Pupil and corneal size measured with an accuracy of 0.01 mm. In vivo and in vitro measurements showed that pupil size increased significantly after ex vivo (p < 0.001), whereas there was no significant difference in eccentricity (p = 0.38). Compared to optical coherence tomography (OCT), the method did not differ significantly in accuracy (p > 0.05), but significantly improved efficiency and reduced reliance on specialized equipment.
The Python-based digital measurement technique can effectively and accurately quantify the morphological parameters of the guinea pig eye, providing reliable technical support for non-contact measurements in experimental animals.
开发一种基于Python的数字技术，用于精确测量豚鼠的瞳孔大小、角膜大小和偏心度，并在眼科研究中针对传统光学相干断层扫描（OCT）方法验证其效率和准确性。
选取14只健康豚鼠，使用相机采集眼部图像，并用Python 3.9编写图像分析程序。该程序集成了边缘检测（Canny算法）、曲线拟合（圆锥曲线方程）和像素-实际距离转换模块，并设计了图形用户界面（GUI）以可视化操作。测量参数包括瞳孔大小、角膜大小和偏心度，以比较体内和体外测量之间的差异并验证该方法的准确性。
Python程序清晰地识别出豚鼠的瞳孔和角膜缘，拟合曲线与实际轮廓高度吻合。瞳孔和角膜大小测量精度为0.01毫米。体内和体外测量显示，体外后瞳孔大小显著增加（p < 0.001），而偏心度无显著差异（p = 0.38）。与光学相干断层扫描（OCT）相比，该方法在准确性上无显著差异（p > 0.05），但显著提高了效率并减少了对专用设备的依赖。
基于Python的数字测量技术能够有效且准确地量化豚鼠眼睛的形态参数，为实验动物的非接触测量提供可靠的技术支持。
