Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5
Jing Zhang, 电子科技大学光电科学与工程学院MOEMIL实验室, 四川 成都 610054, 中国, Yibo Huo, Jialiang Yang, Xiangzhou Wang, Boyun Yan, Xiaohui Du, Ruqian Hao, Fang Yang, Juanxiu Liu, Lin Liu, Yong Liu, Houbin Zhang, 电子科技大学四川省人民医院人类疾病基因研究四川省重点实验室, 四川 成都 610072, 中国
Abstract
Glaucoma is characterized by the progressive loss of retinal ganglion cells (RGCs), although the pathogenic mechanism remains largely unknown. To study the mechanism and assess RGC degradation, mouse models are often used to simulate human glaucoma and specific markers are used to label and quantify RGCs. However, manually counting RGCs is time-consuming and prone to distortion due to subjective bias. Furthermore, semi-automated counting methods can produce significant differences due to different parameters, thereby failing objective evaluation. Here, to improve counting accuracy and efficiency, we developed an automated algorithm based on the improved YOLOv5 model, which uses five channels instead of one, with a squeeze-and-excitation block added. The complete number of RGCs in an intact mouse retina was obtained by dividing the retina into small overlapping areas and counting, and then merging the divided areas using a non-maximum suppression algorithm. The automated quantification results showed very strong correlation (mean Pearson correlation coefficient of 0.993) with manual counting. Importantly, the model achieved an average precision of 0.981. Furthermore, the graphics processing unit (GPU) calculation time for each retina was less than 1 min. The developed software has been uploaded online as a free and convenient tool for studies using mouse models of glaucoma, which should help elucidate disease pathogenesis and potential therapeutics.