【摘要】 从CT图像中快速、准确地分割出新型冠状病毒肺炎(COVID-19)病灶区域，是实现对COVID-19计算机辅助诊疗的重要环节，为此提出了一种基于双注意力机制的COVID-19病灶CT图像分割方法.首先，引入门控注意力AG模块从空间上增强对病灶区域的关注，降低图像亮度不均衡、低对比度对分割精度的影响；其次，引入结合残差单元的SE-Res模块对病灶区域进行通道增强，提取细微结构特征，提高网络对病灶形状变化较大和磨玻璃边界区域的分割性能.在COVID-19公共数据集上实验表明，所提出方法达到的Dice系数、阳性预测值、交并比分别为0.908 8,0.915 2,0.858 9,与前期研究相比，分别提高了0.75%,0.11%,0.65%.所提出方法能提高对病灶形状变化较大区域和磨玻璃边界的分割精度，整体性能优于当前主流模型.更多还原

【Abstract】 Rapid and accurate segmentation of COVID-19 lesions from CT images is an important step to realize computer-assisted diagnosis and treatment of COVID-19. Therefore, a CT image segmentation method of COVID-19 lesions based on dual-attention mechanism is proposed. Firstly, the attention gate module is introduced to enhance the focus on the focal region in space and reduce the influence of image brightness imbalance and low contrast on the segmentation accuracy. Secondly, the SE-Res module combined with residual element was introduced to enhance the channel of the lesion region, extract the fine structural features, and improve the segmentation performance of the network for the lesion shape change and ground glass boundary region. Experiments on the public datasets of COVID-19 show that the Dice, PPV and IoU achieved by the proposed method are 0.908 8, 0.915 2 and 0.858 9, respectively, which are 0.75%, 0.11% and 0.65%higher than previous studies, respectively. The proposed method can improve the segmentation accuracy of the lesions with large shape changes and ground glass boundaries, and the overall performance is better than current mainstream models.更多还原