Department of Biology, Neural Circuit Development and Regeneration Research Group, KU Leuven, Leuven, Belgium.
Glaucoma and Retinal Neurodegenerative Disease Research Group, Institute of Ophthalmology, University College London, London, UK.
Sci Rep. 2021 Jan 12;11(1):702. doi: 10.1038/s41598-020-80308-y.
Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major research efforts are presently directed towards the understanding of disease pathogenesis and the development of new therapies, with the help of rodent models as an important preclinical research tool. The ultimate goal is reaching neuroprotection of the RGCs, which requires a tool to reliably quantify RGC survival. Hence, we demonstrate a novel deep learning pipeline that enables fully automated RGC quantification in the entire murine retina. This software, called RGCode (Retinal Ganglion Cell quantification based On DEep learning), provides a user-friendly interface that requires the input of RBPMS-immunostained flatmounts and returns the total RGC count, retinal area and density, together with output images showing the computed counts and isodensity maps. The counting model was trained on RBPMS-stained healthy and glaucomatous retinas, obtained from mice subjected to microbead-induced ocular hypertension and optic nerve crush injury paradigms. RGCode demonstrates excellent performance in RGC quantification as compared to manual counts. Furthermore, we convincingly show that RGCode has potential for wider application, by retraining the model with a minimal set of training data to count FluoroGold-traced RGCs.
青光眼是一种与视网膜神经节细胞 (RGC) 丧失相关的疾病，仍是全球范围内主要的致盲原因之一。目前，主要的研究工作旨在通过啮齿动物模型作为重要的临床前研究工具，来理解疾病发病机制和开发新疗法。最终目标是实现对 RGC 的神经保护，这需要一种可靠的工具来量化 RGC 的存活情况。因此，我们展示了一种新的深度学习管道，能够在整个鼠视网膜中实现全自动的 RGC 量化。这个名为 RGCode（基于深度学习的视网膜神经节细胞定量）的软件提供了一个用户友好的界面，只需要输入 RBPMS 免疫染色的 flatmounts，就可以返回总的 RGC 计数、视网膜面积和密度，以及显示计算出的计数和等密度图的输出图像。该计数模型是在来自接受微珠诱导性眼高压和视神经挤压损伤模型的小鼠的健康和青光眼视网膜的 RBPMS 染色图像上进行训练的。与手动计数相比，RGCode 在 RGC 量化方面表现出了优异的性能。此外，我们通过用最小的训练数据集重新训练模型来对 FluoroGold 标记的 RGC 进行计数，令人信服地证明了 RGCode 具有更广泛的应用潜力。
