An Enhanced U-Network by Combining PPM and CBAM for Medical Image Segmentation

Zhongming Fu, Hejian Chen, Mengsi He, Li Liu

Abstract: U-network is a comprehensive convolutional neural network that is widely utilized in medical image segmentation domain. However, it is not accurate enough in detail segmentation and resulting in unsatisfactory segmentation results. To solve this problem, this paper proposes an enhanced U-network that combines an improved Pyramid Pooling Module (PPM) and a modified Convolutional Block Attention Module (CBAM). Its whole network is U-Net architecture, where the PPM is improved by reducing the number of bin species and increasing the pooling connection multiples. It is used in the downsampling part of the network, which can extract input image features of various dimensions. And the CBAM is modified by using $1\times 1$ convolutional layers instead of the original fully connected layers. It is used in the upsampling part of the network, which can combine convolution and attention mechanism. This pays attention to the image from two aspects of space and channel. Besides, the network is trained with novel RGB training to further improve the segmentation ability of the network. Experimental results show that our network outperforms traditional U-shaped segmentation networks by 30% to 40% in metrics Dice, IoU, MAE, and BFscore respectively. What‘s more, it is better than U-Net ++, U2-Net, ResU-Net, ResU-Net++, and UNeXt in terms of segmentation effect and training time.