FrFTormer: A Fractional Fourier Transform and Swin Transfomer-Based Neural Network for 3D MRI Brain Tumor Segmentation

The heterogeneity of glioma determines the tumor has different shapes and sizes, and its voxel distribution presented in MRI images is imbalanced. Most existing deep convolutional neural networks or vision Transformer based methods usually process and analyze the raw MRI subjects only in spatial domain, whereas they ignore important signal hidden in tissue structures. We observe that the detailed structural information mainly lies in the high-frequency components, while the high-level semantics are hidden in the low-frequency counterparts. We propose a plug-and-play 3D convolution via Fractional Fourier Transform to extract both low and high frequencies effectively. Then, we design a shifted window self-attention using Swin Transformer for capturing long-range dependencies of the features. More significantly, we construct an encoder-decoder network architecture consisting of a series of 3D Fractional Fourier convolution-based Swin Transformer blocks to learn multi-scale context in a manner of hierarchy. The ablation and comparison experiments on BraTS benchmark demonstrate the effectiveness and medical application potential of the proposed method.