  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.
