Abstract
Microglial cell proliferation in neural tissue (neuroinflammation) occurs during infections, neurological disease, neurotoxicity, and other conditions. In basic science and clinical studies, quantification of microglial proliferation requires extensive manual counting (cell clicking) by trained experts (~ 2 hours per case).
Previous efforts to automate this process have focused on stereology-based estimation of global cell number using deep learning (DL)based segmentation of immunostained microglial cells at high magnification. To further improve on throughput efficiency, we propose a novel approach using snapshot ensembles of convolutional neural networks (CNN) with training using local images, i.e., low (20x) magnification, to predict high or low microglial proliferation at the global level.
An expert uses stereology to quantify the global microglia cell number at high magnification, applies a label of high or low proliferation at the animal (mouse) level, then assigns this global label to each 20x image as ground truth for training a CNN to predict global proliferation. To test accuracy, cross validation with six mouse brains from each class for training and one each for testing was done.
The ensemble predictions were averaged, and the test brain was assigned a label based on the predicted class of the majority of images from that brain. The ensemble accurately classified proliferation in 11 of 14 brains (~ 80%) in less than a minute per case, without cell-level segmentation or manual stereology at high magnification.
This approach shows, for the first time, that training a DL model with local images can efficiently predict microglial cell proliferation at the global level. The dataset used in this work is publicly available at: tinyurl.com/20xData-USF-SRC.