Auto-CSC: A Transfer Learning Based Automatic Cell Segmentation and Count Framework

Cell segmentation and counting play a very important role in the medical field. The diagnosis of many diseases relies heavily on the kind and number of cells in the blood. convolution neural network achieves encouraging results on image segmentation. However, this data-driven method requires a large number of annotations and can be a time-consuming and expensive process, prone to human error. In this paper, we present a novel frame to segment and count cells without too many manually annotated cell images. Before training, we generated the cell image labels on single-kind cell images using traditional algorithms. These images were then used to form the train set with the label. Different train sets composed of different kinds of cell images are presented to the segmentation model to update its parameters. Finally, the pretrained U-Net model is transferred to segment the mixed cell images using a small dataset of manually labeled mixed cell images. To better evaluate the effectiveness of the proposed method, we design and train a new automatic cell segmentation and count framework. The test results and analyses show that the segmentation and count performance of the framework trained by the proposed method equal the model trained by large amounts of annotated mixed cell images.

Automated summary

Cell segmentation and counting are crucial in the medical field, and convolution neural networks have shown promising results in this area. However, the traditional data-driven approach requires a large number of annotations and can be time-consuming and prone to human error. This paper proposes a novel method that eliminates the need for extensive manual annotations by generating cell image labels using traditional algorithms. The proposed method achieves comparable segmentation and count performance to models trained with a large amount of annotated mixed cell images.