基于十字注意力机制改进U-Transformer的新冠肺炎影像分割

COVID-19 Image Segmentation by U-Transformer Improved by Criss cross Attention Mechanism

针对新冠肺炎CT片病灶部分分割检测困难、背景干扰多以及小病灶点易被忽略的问题,提出一种基于注意力机制改进U-Transformer的分割方法。利用注意力机制提升分割精度,修改U-Transformer网络卷积层中间的注意力模块,并提出十字注意力机制,使网络对病灶边缘的分割更为精确。在网络结构中添加全局-局部分割策略,使得对小病灶点的提取更加准确。实验结果表明,改进方法较U-Transformer的精度提高了5.96%,召回率提高了7.11%,样本相似度提高了6.49%,说明改进方法对小病灶点提取具有较好效果。拓展深度学习方法到医疗影像诊断中,有助于放射科医生更快捷、有效地进行病情诊断。

Aiming at the problems of difficult partial segmentation detection,many background interferences and easy neglect of small lesions in new coronary pneumonia CT films,a segmentation method based on attention mechanism to improve U-Transformer is proposed.The atten⁃tion mechanism is used to increase the accuracy of segmentation,and the attention module in the middle of the convolutional layer of the U-Transformer network is modified,and the cross-attention mechanism is used to realize the network to segment the lesion edge more accurately.The whole-local segmentation strategy is added to the network structure to achieve more accurate extraction of small lesion points.The experi⁃mental results show that the improved method improves the accuracy by 5.96%,the recall rate by 7.11%,and the sample similarity by 6.49%compared to the U-Transformer,indicating that the improved method has a good effect on extracting small lesion points.Expanding deep learn⁃ing methods to medical imaging diagnosis can help radiologists diagnose conditions more quickly and effectively.