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

EasyChair Preprint no. 10789

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.

Keyphrases: Brain Tumor, frequency analysis, image segmentation, MRI

@Booklet{EasyChair:10789,
  author = {Hui Ding and Jiacheng Lu and Junwei Cai and Yawei Zhang and Yuanyuan Shang},
  title = {SLf-UNet: Improved UNet for Brain MRI Segmentation by Combining Spatial and Low-Frequency Domain Features},
  howpublished = {EasyChair Preprint no. 10789},

  year = {EasyChair, 2023}}