Non-rigid registration for medical images based on deformable convolution and multi-scale feature focusing modules

非刚性配准在医学图像分析中有着重要的作用。U-Net被证明是医学图像分析的研究热点，被广泛应用于医学图像配准中，然而现有的基于U-Net及其变体的配准模型在处理复杂形变时，学习能力不足，且没有充分利用多尺度上下文信息，导致配准精度不够理想。针对该问题，本文提出了一种基于可变形卷积和多尺度特征聚焦模块的X线图像非刚性配准算法。该算法首先使用残差可变形卷积替代原U-Net的标准卷积，以增强配准网络对图像几何形变的表达能力；然后使用步长卷积代替下采样操作的池化运算，以缓解连续池化导致的特征丢失问题；此外在编、解码结构的桥接层中引入多尺度特征聚焦模块，以提高网络模型集成全局上下文信息的能力。理论分析和实验结果均表明提出的配准算法能聚焦多尺度上下文信息，能够处理具有复杂形变的医学图像，配准精度有一定提高，适合胸部X线片的非刚性配准。.; Non-rigid registration plays an important role in medical image analysis. U-Net has been proven to be a hot research topic in medical image analysis and is widely used in medical image registration. However, existing registration models based on U-Net and its variants lack sufficient learning ability when dealing with complex deformations, and do not fully utilize multi-scale contextual information, resulting insufficient registration accuracy. To address this issue, a non-rigid registration algorithm for X-ray images based on deformable convolution and multi-scale feature focusing module was proposed. First, it used residual deformable convolution to replace the standard convolution of the original U-Net to enhance the expression ability of registration network for image geometric deformations. Then, stride convolution was used to replace the pooling operation of the downsampling operation to alleviate feature loss caused by continuous pooling. In addition, a multi-scale feature focusing module was introduced to the bridging layer in the encoding and decoding structure to improve the network model's ability of integrating global contextual information. Theoretical analysis and experimental results both showed that the proposed registration algorithm could focus on multi-scale contextual information, handle medical images with complex deformations, and improve the registration accuracy. It is suitable for non-rigid registration of chest X-ray images.