Medical image segmentation network based on multi-scale frequency domain filter

With the development of deep learning, medical image segmentation in computer-aided diagnosis has become a research hotspot. Recently, UNet and its variants have become the most powerful medical image segmentation methods. However, these methods suffer from (1) insufficient sensing field and insufficient depth; (2) computational nonlinearity and redundancy of channel features; and (3) ignoring the interrelationships among feature channels. These problems lead to poor network segmentation performance and weak generalization ability. Therefore, first of all, we propose an effective replacement scheme of UNet base block, Double residual depthwise atrous convolution (DRDAC) block, to effectively improve the deficiency of receptive field and depth. Secondly, a new linear module, the Multi-scale frequency domain filter (MFDF), is designed to capture global information from the frequency domain. The high order multi-scale relationship is extracted by combining the depthwise atrous separable convolution with the frequency domain filter. Finally, a channel attention called Axial selection channel attention (ASCA) is redesigned to enhance the network's ability to model feature channel interrelationships. Further, we design a novel frequency domain medical image segmentation baseline method FDFUNet based on the above modules. We conduct extensive experiments on five publicly available medical image datasets and demonstrate that the present method has stronger segmentation performance as well as generalization ability compared to other state-of-the-art baseline methods.