  In this study, we focus on brain tumor sequence registration between
pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain
glioma patients, in the context of Brain Tumor Sequence Registration challenge
(BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in
brain image analysis for quantifying tumor changes. This is a challenging task
due to large deformations and missing correspondences between pre-operative and
follow-up scans. For this task, we adopt our recently proposed Non-Iterative
Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method
for coarse-to-fine registering images with large deformations. To overcome
missing correspondences, we extend the NICE-Net by introducing dual deep
supervision, where a deep self-supervised loss based on image similarity and a
deep weakly-supervised loss based on manually annotated landmarks are deeply
embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a
competitive result on the validation set (mean absolute error: 3.387) and
placed 4th in the final testing phase (Score: 0.3544).
