Abstract: There are some problems such as single prediction scale and information loss in traditional U-Net ultrasound breast image segmentation tasks.To solve these problems,a multi-attention-guided U-Net ultrasound image segmentation method for breast tumors is proposed.Firstly,multiple SEattention module are introduced into the encoding structure of U-Net to extract multi-level semantic information from the input breast tumor images,which guides the encoder to focus on the features of breast tumor and reduces the interference caused by redundant background information.Secondly,by designing a feature fusion processing module,the complex semantic feature fusion processing is carried out on the feature graph from the encoder.Finally,in the decoder part,the pyramid structure is added to capture global spatial information to improve the multi-scale feature extraction ability of the model for tumor images,so as to improve the expression ability and segmentation performance of the whole network.The proposed method is simulated on breast tumor image data set,and the results show that compared with other U-Net improved strategies,the proposed method has better accuracy and robustness.

Key words: Multiple attention guidance, Mammary gland, U-Net, Ultrasound, Image segmentation