A Meta-Learning Approach for Medical Image Registration

TL;DR

This paper introduces a meta-learning based unsupervised model for medical image registration that adapts quickly to new tasks with minimal data, outperforming existing methods in accuracy and training efficiency.

Contribution

The paper presents a novel meta-learning framework for medical image registration that enables rapid adaptation to new domains with limited data, reducing training time and improving accuracy.

Findings

Significantly improved registration accuracy across various medical imaging modalities.

Reduced training time compared to traditional registration models.

Effective adaptation to unseen domain tasks with minimal fine-tuning.

Abstract

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.