Segmentation Method of Magnetoelectric Brain Image Based on the Transformer and the CNN

Abstract

To address the problem of a low accuracy and blurred boundaries in segmenting multimodal brain tumor images using the TransBTS network, a 3D BCS_T model incorporating a channel space attention mechanism is proposed. Firstly, the TransBTS model hierarchy is increased to obtain more local feature information, and residual basis blocks are added to reduce feature loss. Secondly, downsampling is incorporated into the hybrid attention mechanism to enhance the critical region information extraction. Finally, weighted cross-entropy loss and generalized dice loss are employed to solve the inequality problem in the tumor sample categories. The experimental results show that the whole tumor region WT, the tumor core region TC, and the enhanced tumor region ET are improved by an average of 2.53% in the evaluation metric of the Dice similarity coefficient, compared with the TransBTS network and shortened by an average of 3.14 in the metric of Hausdorff distance 95. Therefore, the 3D BCS_T model can effectively improve the segmentation accuracy and boundary clarity of both the tumor core and the enhanced tumor categories of the small areas.

Keywords

• 3D BCS attention mechanism
• transformer
• multiple models
• magnetic resonance imaging
• loss function