Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance.