针对受肺肩区域、胸膈角及肋骨影响的胸部肺野分割问题，提出了一种基于改进U-Net的肺野分割算法。首先，用inception模块代替U-Net编码块中的卷积层，在增加网络宽度的同时捕获更多的图像特征。然后，在编码块与解码块中引入残差网络，提升网络深度的同时保证网络稳定；在编码与解码之间用跳跃连接增强特征的传递和利用，解决编码部分连续下采样中的胸部肺野特征丢失问题。最后，在编码与解码部分结合通道和空间注意力机制对图像特征进行重标定，有效提高了算法的分割精度。实验结果表明，相比其他分割算法，本算法的分割性能更好，在公开Montgomery County数据集上的准确率、召回率、特异性、平均交并比分别为98.90%、97.81%、99.28%、97.17%。

Aiming at the problem that it is difficult to accurately segment the chest lung field affected by the lung shoulder area, thoracic diaphragm angle and ribs, we propose a lung field segmentation algorithm based on improved U-Net. First, the inception module is used to replace the convolutional layer in the U-Net coding block, which can increase the network width while capturing more image features. Then, the residual network is introduced in the coding block and the decoding block to increase the depth of the network and ensure the stability of the network. Skip connections are used between encoding and decoding to enhance the transfer and utilization of features, and to solve the problem of the loss of chest and lung field features due to continuous downsampling in the encoding part. Finally, the channel and spatial attention mechanism are combined in the encoding and decoding parts to analyze the image. Features are re-calibrated to effectively improve the segmentation accuracy of the algorithm. The experimental results show that compared with other segmentation algorithms, the segmentation performance of this algorithm is better. The accuracy, recall rate, specificity, and average intersection ratio on the public Montgomery County data set are 98.90%, 97.81%, 99.28%, and 97.17%, respectively.