Research on neural cell image segmentation based on improved U-Net model

Abstract

Neurological diseases, including Alzheimer's disease and brain tumors, are the leading causes of death and disability worldwide. However, it is difficult for scientists to quantify the response of these deadly diseases to treatment. Existing neuron-based solutions have limited accuracy. Neuroblastoma cell lines have unique, irregular and concave morphology, which makes them show low precision scores in different cancer cell types. Based on this, this study proposes a new cell semantic segmentation network model. The model first enhances the original cell map, and then introduces the residual module and attention mechanism based on the classical U-Net network structure, which alleviates the problem of network degradation and improves the efficiency and effect of network training. The experimental results on the neuroblastoma cell line data set provided by Sartorius show that the segmentation accuracy of the proposed model is about fifteen percentage points higher than that of the classical U-Net model and one percentage point higher than that of the U-Net++ model.