Low Resolution Cell Image Edge Segmentation Based on Convolutional Neural Network

An interesting segmentation method for low-resolution cell images captured by a lens-free imaging system is presented in this paper. The resolution of this cell images is low and its quality has also greatly impacted by the experimental environments, and the traditional cell segmentation methods to solve the original cell images is not robust and sensitive to noise. So based on the convolutional neural network, an optimized CSnet method is proposed in this paper for automatically segmenting cell. In the proposed method, the produced data set will be sent into the convolutional neural network firstly for training to obtain an optimized convolution neural network segmentation model. And then, the pre-divided images acquired by the lens-free imaging system are loaded into the segmentation model to get the segmentation images. Finally, our proposed method in this paper is tested in a neural network framework built in keras. The experimental results show that the accuracy of our proposed method can reach about 96%. At the same time, it also can implement batch segmentation automatically and make the problem of heavy task for segmentation better.