PCU-Net: An enhanced U-network by combining PPM and CBAM for Medical Image Segmentation

Hejian Chen, Zhongming Fu, Li Liu, Mengsi He, Jiayi Deng, Zhuo Tang

Abstract: U-network is a kind of full convolutional neural network, which is widely used in the field of medical image segmentation. However, it still has the problem of small targets being unable to be segmented and resulting in unsatisfactory segmentation effect. In order to solve this problem, this paper proposes an enhanced U-network by combining pyramid pooling module (PPM) and convolutional block attention module (CBAM). It’s whole network is U-Net architecture, where PPM with bin sizes of 1×1, 2×2 and 3×3 are used in the downsampling part of the network, which can extract input image features of various dimensions. And CBAM is used in the downsampling part of the network, which combines convolution and attention mechanism, and can pay attention to the image from two aspects of space and channel to improve the segmentation ability of the network. Experimental results show that our network outperforms traditional Ushaped segmentation networks by 30% to 40% in metrics IoU, MAE, and Dice, respectively.