Automatic Cell Counting using Active Deep Learning and Unbiased Stereology

Abstract

Training deep learning models for unbiased stereology requires a large data set with associated ground truth. However manual ground truth annotation is tedious, time-consuming, and expert dependent. We propose an active deep learning method for automatic stereology counts using a snapshot ensemble approach. The method provides a confidence score for each mask in an unlabeled pool that reduces user verification to only images with high information content for training the deep learning model. The proposed method reduces the error rate to less than 1% for unbiased stereology cell counts on immunostained brain cells compared to manual stereology and requires $\\sim 25$% less expert verification time compared to a previously proposed iterative deep learning approach.