On-the-Fly Guidance Training for Medical Image Registration

TL;DR

This paper presents a new training framework called On-the-Fly Guidance (OFG) that improves learning-based medical image registration models by generating pseudo-ground truth during training, eliminating the need for labeled data and enhancing performance.

Contribution

The paper introduces OFG, a supervised training approach that refines deformation predictions with a differentiable optimizer, boosting registration accuracy without additional inference costs.

Findings

Significantly improved registration performance on benchmark datasets.

Compatible with existing models as a plug-and-play training enhancement.

Does not require labeled data, reducing data annotation needs.

Abstract

This study introduces a novel On-the-Fly Guidance (OFG) training framework for enhancing existing learning-based image registration models, addressing the limitations of weakly-supervised and unsupervised methods. Weakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy. Our method proposes a supervised fashion for training registration models, without the need for any labeled data. OFG generates pseudo-ground truth during training by refining deformation predictions with a differentiable optimizer, enabling direct supervised learning. OFG optimizes deformation predictions efficiently, improving the performance of registration models without sacrificing inference speed. Our method is tested across several benchmark datasets and leading models, it significantly enhanced performance, providing a plug-and-play solution for training learning-based registration models. Code available at: https://github.com/cilix-ai/on-the-fly-guidance