FD-DUNet: Frequency Domain Global Modeling Enhances Receptive Field Expansion UNet for Efficient Medical Image Segmentation

Medical image segmentation is crucial for computer-aided diagnosis, facilitating lesion identification. U-shaped structures have been prevalent in this domain, yet traditional methods employing Convolutional Neural Networks (CNNs) face challenges in modeling long-range dependencies. Consequently, there is a shift towards integrating global modeling mechanisms with CNNs. However, prevailing methods for global feature extraction in the spatial domain often encounter quadratic complexity, resulting in high parameters and rendering training on limited datasets less than optimal. To address this issue, we propose FD-DUNet, an efficient model with a global-local interaction architecture. The global encoder incorporates the Frequency Domain Global Modeling (FDGM) blocks, leveraging fast Fourier transform for capturing long-range dependencies with log-linear complexity. Meanwhile, the local encoder features Receptive Field Expansion (RFE) blocks, gradually widening the receptive field to extract fine-grained features. Additionally, our novel lightweight decoder combines a multi-path feature aggregation approach with the zero-parameter Spatial-shift operation to integrate global-local information effectively. Experimental evaluations on ISIC-2018 and BUSI datasets demonstrate the superior performance of FD-DUNet over existing methods, showing its efficiency with only 3.70 GFLOPs and 3.28 million parameters.