  Non-rigid registration is a necessary but challenging task in medical imaging
studies. Recently, unsupervised registration models have shown good
performance, but they often require a large-scale training dataset and long
training times. Therefore, in real world application where only dozens to
hundreds of image pairs are available, existing models cannot be practically
used. To address these limitations, we propose a novel unsupervised
registration model which is integrated with a gradient-based meta learning
framework. In particular, we train a meta learner which finds an initialization
point of parameters by utilizing a variety of existing registration datasets.
To quickly adapt to various tasks, the meta learner was updated to get close to
the center of parameters which are fine-tuned for each registration task.
Thereby, our model can adapt to unseen domain tasks via a short fine-tuning
process and perform accurate registration. To verify the superiority of our
model, we train the model for various 2D medical image registration tasks such
as retinal choroid Optical Coherence Tomography Angiography (OCTA), CT organs,
and brain MRI scans and test on registration of retinal OCTA Superficial
Capillary Plexus (SCP). In our experiments, the proposed model obtained
significantly improved performance in terms of accuracy and training time
compared to other registration models.
