DenseTrans: Multimodal Brain Tumor Segmentation Using Swin Transformer

Abstract

Aiming at the task of automatic brain tumor segmentation, this paper proposes a new DenseTrans network. In order to alleviate the problem that convolutional neural networks(CNN) cannot establish long-distance dependence and obtain global context information, swin transformer is introduced into UNet++ network, and local feature information is extracted by convolutional layer in UNet++. then, in the high resolution layer, shift window operation of swin transformer is utilized and self-attention learning windows are stacked to obtain global feature information and the capability of long-distance dependency modeling. meanwhile, in order to alleviate the secondary increase of computational complexity caused by full self-attention learning in transformer, deep separable convolution and control of swin transformer layers are adopted to achieve a balance between the increase of accuracy of brain tumor segmentation and the increase of computational complexity. on BraTs2021 data validation set, model performance is as follows: the dice dimilarity score was 93.2%,86.2%,88.3% in the whole tumor,tumor core and enhancing tumor, hausdorff distance(95%) values of 4.58mm,14.8mm and 12.2mm, and a lightweight model with 21.3M parameters and 212G flops was obtained by depth-separable convolution and other operations. in conclusion, the proposed model effectively improves the segmentation accuracy of brain tumors and has high clinical value.

Keywords

• Brain tumor segmentation
• convolutional neural networks
• swin transformer
• UNet++