Automatic microscopic cell counting by use of deeply-supervised density regression model
This addresses the tedious and error-prone task of manual cell counting for medical and biological applications, representing an incremental improvement in automated methods.
The paper tackled the problem of inaccurate automatic cell counting in microscopic images by proposing a deeply-supervised density regression model, which improved accuracy through auxiliary supervision and multi-scale feature integration.
Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired. To improve the accuracy of automatic cell counting, we propose here a novel method that employs deeply-supervised density regression. A fully convolutional neural network (FCNN) serves as the primary FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are employed to provide additional supervision for learning the intermediate layers of the primary CNN to improve network performance. In addition, the primary CNN is designed as a concatenating framework to integrate multi-scale features through shortcut connections in the network, which improves the granularity of the features extracted from the intermediate CNN layers and further supports the final density map estimation.