基于视觉变换器的级联多阶层医学影像配准方法

Cascaded multi-level medical image registration method based on transformer

在基于深度学习的图像配准中,图像中具有复杂解剖结构的形变区域是影响网络配准精度的重要因素,然而现有方法很难关注到图像的复杂解剖区域。同时,卷积神经网络的感受野受其卷积核大小的限制,难以学习空间位置距离较远的体素之间的关系,使其难以处理较大区域形变问题。针对以上两个问题,本文提出了一种基于视觉变换器(Transformer)的级联多阶层配准网络模型,并配备了一种基于均方误差的困难形变感知机。困难形变感知机使用滑动窗口和浮动窗口技术在配准图像中进行检索,得到每个体素的困难形变系数,识别出配准效果最差的区域。本研究中,级联多阶层配准网络模型采用困难形变感知机进行阶层连接,在基础配准网络中凭借自注意力机制提取全局特征,对不同尺度的配准结果进行优化。实验结果证明,本文提出的方法可以对复杂形变区域进行渐进配准,从而优化脑部医学影像的配准结果,对医生的临床诊断工作有良好的辅助作用。

In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.