SLf-UNet: Improved UNet for Brain MRI Segmentation by Combining Spatial and Low-Frequency Domain Features

Hui Ding, Jiacheng Lu, Junwei Cai, Yawei Zhang, Yuanyuan Shang

Abstract: Deep learning-based methods have shown remarkable performance in brain tumor image segmentation. However, there is a lack of research on segmenting brain tumor lesions using frequency domain features of images. To address this gap, an improved network SLf-UNet has been proposed in this paper, which is a two-dimensional encoder-decoder architecture combining spatial and low-frequency domain features based on U-Net. The proposed model effectively learns information from spatial and frequency domains. Herein, we present a novel upsample approach by using zero padding in the high-frequency region and replacing the part of the convolution operation with a convolution block combining spatial frequency domain features. Our experimental results demonstrate that our method outperforms current mainstream approaches on BraTS 2019 and BraTS 2020 datasets. Code is available soon at https://github.com/noseDewdrop/SLf-UNet.