Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model - Shuli Xing; Zhenwei Lai; Junxiong Zhu; Wenwu He; Guojun Mao (Applied Sciences)
The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. However, Convolution-based models often fail to deliver precise segmentation results, while Transformer-based models often require more computational resources. To address these challenges, we propose a novel hybrid model named Local–Global UNet Transformer. In our model, we introduce: (1) a semantic-oriented masked attention to enhance the feature extraction capability of the decoder; and (2) network-in-network blocks to increase channel modeling complexity in the encoder while reducing the parameter consumption associated with residual blocks. We evaluate our model on two public brain tumor segmentation datasets, and the experimental results demonstrate that our model achieves the highest average Dice score on the BraTS2024-GLI dataset and ranks second on the BraTS2023-GLI dataset. In terms of <semantics>HD95</semantics>, our model attains the lowest values on both datasets. Furthermore, the ablation study proves the effectiveness of our model design.