Lung Tumor Segmentation Method Based on Transformer and Attention Mechanisms

The accurate segmentation of lung tumors plays a crucial role in tumor diagnosis and treatment. However, lung tumor segmentation is often challenged by several issues such as low contrast between lesions and surrounding tissues, tumor-normal tissue adhesion, and high background noise. To address these, this study introduces a lung tumor segmentation method based on Transformer and attention mechanisms. In the Transformer encoder stage, both global and local attention mechanisms are incorporated to enable the network to simultaneously focus on both global and local contextual information. In the skip connection stage, a channel-prior convolutional attention mechanism is utilized to enhance the spatial perception ability for complex lesions and reduce the channel dimension redundancy, such that the tumor segmentation accuracy can be improved. The experimental results on the private GDPH and public LUNG1 datasets demonstrate that the proposed method outperforms eight comparative methods in terms of the Dice metric by achieving approximately 90.96% and 88.18% on the two datasets, respectively. The proposed method can provide reliable assistance for clinical diagnosis and treatment.