Applications of Automatic Unbiased Stereology to Neural Tissue

Stereology methods are the current best practice for state-of-the-art quantification of stained biostructures in tissue sections. Over the past six decades, stereological applications to bioscience research have added knowledge about neurostructural changes in normal aging, age-related neurodegeneration (Alzheimer’s, Parkinson’s, ALS), oncology, toxic exposures, learning and memory behavior, a range of mental illnesses, and drug discovery to name a few. In current practice, however, all computer-assisted stereology systems require time-consuming manual data collection by well-trained experts, which is prone to inter-rater error due to the variation in training, experience, motivation, and fatigue. Recent work shows these limitations can be overcome by novel developments in artificial intelligence (AI)-based deep learning (DL) as shown in other domains for solving similar problems. In this chapter, we review basic stereology concepts for avoiding bias, tissue processing, and tissue sampling for quantifying total cell number in tissue sections and show how DL approaches can automatically collect stereology data with comparable accuracy but superior precision (reproducibility) and throughput efficiency as manual stereology methods. Finally, we identify the major challenges to automation using DL approaches and review novel applications to the quantification of stained biostructures using unbiased methods.