Abstract
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 …
本文提出边缘导向Transformer（EoFormer），用于脑肿瘤MRI图像分割。该方法采用CNN-Transformer混合编码器，CNN提取局部低级特征，Transformer建模长距离依赖以生成全局高级特征；解码器集成边缘导向Sobel与Laplacian锐化模块，增强边缘信息。同时引入高效注意力与重参数化技术，提升特征表示能力与分割精度。