Lung segmentation in chest X-ray images is of paramount importance as it plays a crucial role in the diagnosis and treatment of various lung diseases. This paper presents a novel approach for lung segmentation in chest X-ray images by integrating U-net with attention mechanisms. The proposed method enhances the U-net architecture by incorporating a Convolutional Block Attention Module (CBAM), which unifies three distinct attention mechanisms: channel attention, spatial attention, and pixel attention. The channel attention mechanism enables the model to concentrate on the most informative features across various channels. The spatial attention mechanism enhances the model's precision in localization by focusing on significant spatial locations. Lastly, the pixel attention mechanism empowers the model to focus on individual pixels, further refining the model's focus and thereby improving the accuracy of segmentation. The adoption of the proposed CBAM in conjunction with the U-net architecture marks a significant advancement in the field of medical imaging, with potential implications for improving diagnostic precision and patient outcomes. The efficacy of this method is validated against contemporary state-of-the-art techniques, showcasing its superiority in segmentation performance.

翻译：胸部X光图像中的肺部分割在多种肺部疾病的诊断与治疗中具有至关重要的作用。本文提出了一种融合U-net与注意力机制的胸部X光肺部分割新方法。该方法通过引入卷积块注意力模块对U-net架构进行增强，该模块统一了三种不同的注意力机制：通道注意力、空间注意力和像素注意力。通道注意力机制使模型能够聚焦于不同通道中最具信息量的特征；空间注意力机制通过关注显著空间位置提升模型定位精度；像素注意力机制则使模型能够聚焦于单个像素点，进一步细化关注区域从而提高分割精度。所提出的卷积块注意力模块与U-net架构的联合应用标志着医学影像领域的显著进步，对提升诊断准确性和患者预后具有潜在意义。该方法通过与当代最先进技术的对比验证其有效性，在分割性能上展现出优越性。