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

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.

Automatic stereology of mean nuclear size of neurons using an active contour framework.

The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 µm. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R2 ≥ 0.95) with a 5× gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.

A novel image segmentation method for the evaluation of inflammation-induced cortical and hippocampal white matter injury in neonatal mice.

The developing brain is very susceptible to environmental insults, and very immature infants often suffer from long-term neurological syndromes associated with white matter injuries such as periventricular leukomalacia. Infection and inflammation are important risk factors for neonatal brain white matter injuries, but the evaluation of white matter injury in animal models, especially the quantification of myelinated axons, has long been problematic due to the lack of ideal measurement methods. Here, we present an automated segmentation method, which we call MyelinQ, for the quantification of myelinated white matter in immunohistochemical DAB-stained sections of the neonatal mouse brain. Using MyelinQ, we show that a viral infection mimic agent, the Toll-like receptor 3 ligand Poly I:C, causes significant hypomyelination of white matter in the cortical and hippocampal fimbria regions, but not in the striatal caudoputamen region. We showed that MyelinQ can reliably produce results that are comparable to a method used in our previous publications. However, in comparison to the conventional method, MyelinQ has the advantages of being automated, objective and accurate. MyelinQ can analyze white matter in various specific brain regions and therefore provides a useful platform for the quantification of myelin and the evaluation of white matter injuries in animal models. MyelinQ and its code together with instructions for use can be found at: https://github.com/parham-ap/myelinq.