Volume 43 Issue 5
Sep.  2022
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Jing Zhang, Yi-Bo Huo, Jia-Liang Yang, Xiang-Zhou Wang, Bo-Yun Yan, Xiao-Hui Du, Ru-Qian Hao, Fang Yang, Juan-Xiu Liu, Lin Liu, Yong Liu, Hou-Bin Zhang. Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5. Zoological Research, 2022, 43(5): 738-749. doi: 10.24272/j.issn.2095-8137.2022.025
Citation: Jing Zhang, Yi-Bo Huo, Jia-Liang Yang, Xiang-Zhou Wang, Bo-Yun Yan, Xiao-Hui Du, Ru-Qian Hao, Fang Yang, Juan-Xiu Liu, Lin Liu, Yong Liu, Hou-Bin Zhang. Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5. Zoological Research, 2022, 43(5): 738-749. doi: 10.24272/j.issn.2095-8137.2022.025

Automatic counting of retinal ganglion cells in the entire mouse retina based on improved YOLOv5

doi: 10.24272/j.issn.2095-8137.2022.025
Funds:  This work was supported by the National Natural Science Foundation of China (61405028 to J.Z., 81770935 to H.B.Z.), Fundamental Research Funds for the Central Universities (University of Electronic Science and Technology of China) (ZYGX2019J053 to J.Z.), and Department of Science and Technology of Sichuan Province, China (2020YJ0445 to H.B.Z.)
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  • 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.
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