Deformable image registration (alignment) is highly sought after in numerous
clinical applications, such as computer aided diagnosis and disease progression
analysis. Deep Convolutional Neural Network (DCNN)-based image registration
methods have demonstrated advantages in terms of registration accuracy and
computational speed. However, while most methods excel at global alignment,
they often perform worse in aligning local regions. To address this challenge,
this paper proposes a mask-guided encoder-decoder DCNN-based image registration
method, named as MrRegNet. This approach employs a multi-resolution encoder for
feature extraction and subsequently estimates multi-resolution displacement
fields in the decoder to handle the substantial deformation of images.
Furthermore, segmentation masks are employed to direct the model's attention
toward aligning local regions. The results show that the proposed method
outperforms traditional methods like Demons and a well-known deep learning
method, VoxelMorph, on a public 3D brain MRI dataset (OASIS) and a local 2D
brain MRI dataset with large deformations. Importantly, the image alignment
accuracies are significantly improved at local regions guided by segmentation
masks. Github link:this https URL

提出了一种名为 MrRegNet 的基于掩膜引导的编码器 - 解码器深度卷积神经网络（DCNN）图像配准方法，通过多分辨率编码器提取特征并在解码器中估计多分辨率位移场来处理图像的大变形，并使用分割掩膜指导模型的注意力对齐局部区域，实验结果证明该方法在公开的 3D 脑 MRI 数据集（OASIS）和局部 2D 脑 MRI 数据集上的大变形图像中优于传统方法（如 Demons）和知名的深度学习方法 VoxelMorph，尤其在由分割掩膜引导的局部区域图像配准准确性显著提高。