  Accurately counting the number of cells in microscopy images is required in
many medical diagnosis and biological studies. This task is tedious,
time-consuming, and prone to subjective errors. However, designing automatic
counting methods remains challenging due to low image contrast, complex
background, large variance in cell shapes and counts, and significant cell
occlusions in two-dimensional microscopy images. In this study, we proposed a
new density regression-based method for automatically counting cells in
microscopy images. The proposed method processes two innovations compared to
other state-of-the-art density regression-based methods. First, the density
regression model (DRM) is designed as a concatenated fully convolutional
regression network (C-FCRN) to employ multi-scale image features for the
estimation of cell density maps from given images. Second, auxiliary
convolutional neural networks (AuxCNNs) are employed to assist in the training
of intermediate layers of the designed C-FCRN to improve the DRM performance on
unseen datasets. Experimental studies evaluated on four datasets demonstrate
the superior performance of the proposed method.
