Segmentation of Cell Images Based on Improved Deep Learning Approach
Chuanbo Huang, Huali Ding, Chuanling Liu
Abstract
The improved U_net algorithm based on mixed convolution blocks (McbUnet), which combines the advantages of U-Net and residual learning, is proposed for cell image segmentation in this paper. The network is mainly composed of two kinds of mixed convolution blocks. There are three main benefits to this algorithm. First, the convolution block can utilize different size kernels to overcome the limitation of a single size convolution kernel in traditional deep convolution. Second, in the mixed convolution blocks, two hyperparameters (Width multiplier and Resolution multiplier) are used to quickly adjust the model to fit a specific environment. Third, the residual paths are improved. We test the proposed network and compare it with other recent segmentation methods based on deep learning. The proposed method is superior to comparison methods, which shows its effectiveness.