MSCA-UNet: multi-scale channel attention-based UNet for segmentation of medical ultrasound images

Since deep learning is introduced to medical image segmentation, UNet and its variants have been extensively applied in medical image analysis. This paper proposes a multi-scale channel attention UNet (MSCA-UNet) to raise the accuracy of the segmentation in medical ultrasound images. Specifically, a multi-scale module is constructed to connect and to enhance the feature maps with different scales extracted by convolution. Subsequently, A channel attention mechanism is designed to compress feature maps through learnable depth separable convolutions. Eventually, we have explored the global feature module to establish the dependency between multi-level features. The proposed method is thoroughly evaluated and compared with the existing methods on four medical ultrasound image datasets. The experiments indicate that our method outperforms the SOTA method in accuracy on four medical ultrasound image datasets. Compared with UNet network, the parameters of our model have decreased 29.82%. In addition, visual comparisons further demonstrate the proposed method.