Recurrent Inference Machine for Medical Image Registration

TL;DR

This paper introduces RIIR, a novel recurrent inference network for medical image registration that improves accuracy and data efficiency by learning optimization updates, outperforming existing methods especially with limited training data.

Contribution

The paper presents RIIR, a meta-learning based recurrent inference model that enhances registration accuracy and data efficiency in medical imaging tasks.

Findings

RIIR outperforms existing deep learning registration methods.

RIIR achieves high accuracy with only 5% of training data.

Recurrent hidden states significantly improve registration performance.

Abstract

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advancements in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modelling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver to the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We evaluated RIIR extensively on brain MRI and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only $5\%$ of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.