Lung segmentation in chest X-ray images is a critical task in medical image analysis, enabling accurate diagnosis and treatment of various lung diseases. In this paper, we propose a novel approach for lung segmentation by integrating Hierarchical SegNet with a proposed multi-modal attention mechanism. The channel attention mechanism highlights specific feature maps or channels crucial for lung region segmentation, while the context attention mechanism adaptively weighs the importance of different spatial regions. By combining both mechanisms, the proposed mechanism enables the model to better capture complex patterns and relationships between various features, leading to improved segmentation accuracy and better feature representation. Furthermore, an attention gating mechanism is employed to integrate attention information with encoder features, allowing the model to adaptively weigh the importance of different attention features and ignore irrelevant ones. Experimental results demonstrate that our proposed approach achieves state-of-the-art performance in lung segmentation tasks, outperforming existing methods. The proposed approach has the potential to improve the accuracy and efficiency of lung disease diagnosis and treatment, and can be extended to other medical image analysis tasks.

本研究提出了一种将分层SegNet与多模态注意力机制相结合的新方法用于肺分割，实验证明该方法在肺分割任务中表现优异，超过现有方法，具有提高肺病诊断和治疗准确性和效率的潜力，并可推广到其他医学图像分析任务。

基于通道和上下文注意力的分层SegNet用于胸部X光图像的准确肺部分割