Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex

Abstract

Collection of unbiased stereology data currently relies on relatively simple, low throughput technology developed in the mid-1990s. In an effort to improve the accuracy and efficiency of these integrated hardware-software-digital microscopy systems, we have developed an automatic segmentation algorithm (ASA) for automatic stereology counts using the unbiased optical fractionator method. Here we report on a series of validation experiments in which immunostained neurons (NeuN) and microglia (Iba1) were automatically counted in tissue sections through a mouse neocortex. In the first step, a minimum of 100 systematic-random z-axis image stacks (disector stacks) containing NeuN- and Iba1-immunostained cells were automatically collected using a software-controlled 3 axes (XYZ) stage motor. In the second step, each disector stack was converted to an extended depth of field (EDF) image in which each cell is shown at its optimal plane of focus. Third, individual neurons and microglia were segmented and the regional minimas were extracted and used as seed regions for cells in a watershed segmentation algorithm. Finally, the unbiased disector frame and counting rules were used to make unbiased parameter estimates for neurons and microglia cells. The results for both NeuN neurons and Iba1 microglia were compared to manual counts made by a moderately experienced data collector from the same disector stacks. The final results show lower error rates for counts of Iba1-immunostained microglia cells than for quantifying NeuN-immunostained neurons, most likely due to less three-dimensional overlapping of Iba1 cells. We report that the throughput efficiency of using ASA to automatically annotate images of Iba1 microglia is more than five 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.