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:https://github.com/ruizhe-l/MrRegNet.

翻译：可变形图像配准（对齐）在众多临床应用中备受关注，例如计算机辅助诊断和疾病进展分析。基于深度卷积神经网络（DCNN）的图像配准方法在配准精度和计算速度方面展现出优势。然而，尽管大多数方法擅长全局对齐，但在局部区域的对齐效果往往较差。为解决这一挑战，本文提出一种基于掩膜引导的编码器-解码器DCNN图像配准方法，命名为MrRegNet。该方法采用多分辨率编码器进行特征提取，随后在解码器中估计多分辨率位移场以处理图像的显著形变。此外，利用分割掩膜引导模型关注局部区域的对齐。实验结果表明，在具有大形变的公开3D脑MRI数据集（OASIS）和本地2D脑MRI数据集上，所提方法优于传统方法（如Demons）以及知名深度学习方法VoxelMorph。重要的是，在分割掩膜引导下，局部区域的图像对齐精度得到显著提升。GitHub链接：https://github.com/ruizhe-l/MrRegNet。