融合特征金字塔与可变形分离卷积的CT图像层间插值方法

Inter-Layer Interpolation Method of CT Images Combined with Feature Pyramid and Deformable Separable Convolution

针对计算机断层扫描(CT)序列图像中层间分辨率远低于层内分辨率的问题,提出了一种融合特征金字塔与可变形分离卷积的CT图像层间插值网络。该网络由两个模块构成:图像生成模块和图像增强模块。图像生成模块利用MultiResUNet实现对输入图像的特征提取,然后利用两组不同的可变形分离卷积分别对输入图像进行卷积运算生成候选层间图像;图像增强模块通过特征金字塔和图像合成网络实现对输入图像的多尺度特征融合,生成额外专注于上下文细节的图像,以进一步对候选层间图像的纹理细节进行增强。实验结果表明,所提层间插值网络生成的层间图像在定性和定量分析上均取得了较优效果,在图像边缘轮廓及纹理细节处理上表现更加出色,能有效提高CT序列图像的层间分辨率。

Aiming at the problem that the inter-layer resolution of computed tomography(CT)sequence images is much lower than the intra-layer resolution,an inter-layer interpolation network for CT images combined with feature pyramid and deformable separated convolution is proposed.The network consists of two modules,the image generation module and the image enhancement module.The image generation module utilizes the MultiResUNet to achieve feature extraction of the input image,and uses two different sets of deformable separation convolutions to generate candidate inter-layer images by performing convolution operations on the input image respectively.The image enhancement module fuses the multi-scale features of the input image through the feature pyramid and the image synthesis network,and generates additional images focusing on contextual details to further enhance the texture details of the candidate inter-layer images.The experimental results show that the inter-layer images generated by the proposed inter-layer interpolation network achieve better results in both qualitative and quantitative analysis,and perform better in the processing of image edge contours and texture details,which can effectively improve the inter-layer resolution of CT sequence images.