Enhanced Center Coding for Cell Detection with Convolutional Neural Networks
This work addresses a specific bottleneck in biomedical cell analysis, offering incremental improvements for researchers in that domain.
The paper tackles the problem of coding raw dot labels for cell detection and counting by proposing a new coding scheme that improves training ease and label recovery robustness, resulting in up to 12% higher counting accuracy and 14% higher detection accuracy compared to existing methods.
Cell imaging and analysis are fundamental to biomedical research because cells are the basic functional units of life. Among different cell-related analysis, cell counting and detection are widely used. In this paper, we focus on one common step of learning-based cell counting approaches: coding the raw dot labels into more suitable maps for learning. Two criteria of coding raw dot labels are discussed, and a new coding scheme is proposed in this paper. The two criteria measure how easy it is to train the model with a coding scheme, and how robust the recovered raw dot labels are when predicting. The most compelling advantage of the proposed coding scheme is the ability to distinguish neighboring cells in crowded regions. Cell counting and detection experiments are conducted for five coding schemes on four types of cells and two network architectures. The proposed coding scheme improves the counting accuracy versus the widely-used Gaussian and rectangle kernels up to 12%, and also improves the detection accuracy versus the common proximity coding up to 14%.