A Novel Convolutional Regression Network for Cell Counting

Abstract

A stacked deep convolutional neural network (DCNN) model was generated to predict cell density maps and count cells. We treated the cell counting as a regression problem with a preprocessing step to generate cell density maps. We implemented this approach by integrating two trustworthy and state-of-art model architectures (U-net & VGG19). This method combines the advantages from both traditional segmentation-based and density-based methods. It overcomes the limitations such as cell clumping, overlapping, and it can also bypass the fine-tuning step which was necessary for previous density-based methods when applying to different datasets. A publicly available well-labeled dataset was used to train and test the model. An unlabeled real dataset which generated in-house was used to evaluate the performance.