Automatic Ground Truth for Deep Learning Stereology of Immunostained Neurons and Microglia in Mouse Neocortex

We report that the throughput efficiency of using ASA to automatically annotate images of Iba1 microglia is more than 5 times greater than that of manual stereology counts of the same sections. Moreover, we show that ASA is significantly more accurate in counting microglia cells than a moderately experienced data collector (about 10% higher overall accuracy) when both were compared to counts by an expert neurohistologist. Thus, the ASA method applied to EDF images from disector stacks can be extremely useful to automate and increase the accuracy of cell counts, which could be especially helpful and cost-effective when expert help is not available. Another potential use of our ASA approach is to generate unsupervised ground truth as an efficient alternative to manual annotation for training deep learning models, as shown in our ongoing work.
Source: Journal of Chemical Neuroanatomy - Category: Neuroscience Source Type: research