Abstract
Abstract in English, Chinese 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. 青光眼是一种特征为视网膜神经节细胞（RGC）持续性丢失的疾病，其发病机制尚不明确。为了研究青光眼的发病机制并评估RGC的减少过程，通常使用小鼠模型来模拟人类青光眼，并使用特定标记物标记量化RGC的数量。人工手动计算RGC数量的方式非常耗时，并且很容易收到主观偏见的影响。半自动计数的方式会因为参数设置不同而导致结果存在较大的差异，这不符合客观计算的要求。为了提高计数的准确性和效率，该文提出了一种基于改进YOLOv5的全自动计数算法，该算法使用五通道代替单个通道，并添加注意力机制提高计数精度。在整个小鼠视网膜RGC数量的计算过程中，先通过将视网膜分割成小的部分重叠区域并单独计数，然后使用非最大抑制算法将所有分割区域计算结果汇总在一起得到最终个数。自动计数结果显示，该文的算法与手动计数结果有很强的相关性（平均皮尔逊相关系数约等于0.993），而且模型的平均精准度AP达到了0.981。此外，每个小鼠视网膜在GPU下的计算时间少于一分钟，全自动计数的软件已经在网上开源供大家免费使用，它将为使用小鼠模型进行青光眼研究的研究人员提供一个方便的工具，以了解青光眼的发病机制并开发潜在的治疗药物。.