EoFormer: Edge-Oriented Transformer for Brain Tumor Segmentation

Accurate segmentation of brain tumors in MRI images requires precise detection of the edges. However, this crucial information has been overlooked by existing methods. In this paper, we introduce the Edge-oriented Transformer (EoFormer) which specifically captures and enhances edge information for brain tumor segmentation. Our approach incorporates a CNN-Transformer encoder to comprehensively improve the feature representation capability. The CNN structure captures low-level local features in the image, while the Transformer structure establishes long-range dependencies between features to generate high-level global features. Additionally, the decoder of our approach utilizes two edge sharpening modules, the Edge-oriented Sobel and Laplacian modules, which enhance the edge information. We also introduce efficient attention and re-parameterization techniques that make EoFormer computationally efficient. Experimental results on the BraTS 2020 dataset and a private medulloblastoma dataset demonstrate the superiority of our approach compared with existing state-of-the-art methods. Moreover, our method achieves this with limtied model parameters and lower FLOPs, making it a promising approach for future research. The code is available at https://github.com/sd0809/EoFormer .