A Foreground Mask Network for Cell Counting

Cell counting is important in medical image analysis for its meaningful information. In this paper, we propose a cell counting network to predict the number of cells in an image with the distribution of cells. The proposed network learns to predict the density map which has a direct relationship with the number of cells. A foreground mask is designed to filter the low-level feature maps and the favorable information is fed to the decoder to recover the spatial information better. The foreground mask is a probability map indicating the pixels are more likely to belong to cells. Experiments on three public datasets show that the proposed model can achieve promising performances. Especially the ablation study on the Adipocyte Cells demonstrates the necessity of the foreground mask.