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

• Published in: Journal of chemical neuroanatomy 2019-07, Vol.98, p.1-7
• Main Authors: Ahmady Phoulady, Hady, Goldgof, Dmitry, Hall, Lawrence O., Mouton, Peter R.
• Format: Article
• Language: English
• Subjects: Cell counting; Gold standard; Manual ground truth; Microglia; Neural network; Neuron; Optical fractionator; Segmentation; Stereology
• ISSN: 0891-0618; 1873-6300
• DOI: 10.1016/j.jchemneu.2019.02.006

•We proposed an Automated Segmentation Algorithm (ASA) to segment neurons and microglia cells in NeuN- and Iba1-stained images.•The proposed algorithm is used to automate the stereology task of counting cells.•ASA segments cells in Extended Depth of Field (EDF) images using a combination of several image processing techniques such as Gaussian Mixture Model (GMM), morphological operations, watershed segmentation, Voronoi diagrams and boundary smoothing in a novel approach.•The experiments for neuron counting, show a high correlation between the automated neuron counts and manual counts.•It has been observed that the automated microglia counts are more accurate than the manual counts on a (non-expert) data collectors when compared to the counts of an expert.•Moreover, the segmentation output of ASA is shown to be useful in training a deep learning neural network model (in ongoing work).•The results show that ASA can be reliably used to automate the time-consuming task of cell counting, and it can potentially be revised and extended to count other types of cells in mouse neocortex images. 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