Figure 6
From: A novel retinal ganglion cell quantification tool based on deep learning
Transfer learning of RGCode for Fluorogold labelling. (a, b) Linear regression and Bland Altman analysis (mean bias ± 95% limits of agreement) after running RGCode on Fluorogold-labelled RGCs. Counting performance was considerably lower as compared to the RBPMS dataset, whereas a higher bias was observed. (c) the lower performance of RGCode on FluoroGold-traced flatmounts resulted in a high variability in density (mean ± SEM, unpaired, two-tailed t test, **p = 0.0042; ***p = 0.0007). (d) Composition of the training and testing dataset used for transfer learning. (e,f) Transfer learning of RGCode with a minimal set of new training data reveals a high accuracy with linear regression and Bland–Altman analysis. Mean bias ± 95% limits of agreement are depicted. (g) Transfer learning results in a lower variability in the RGC density measurements. As expected, the density of FluoroGold+ RGCs is significantly lower as compared to RBPMS+ ones (unpaired, two-tailed t test, ***p = 0.007).
