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

翻译：本研究提出了一种新颖的实时引导（OFG）训练框架，用于增强现有基于学习的图像配准模型，以解决弱监督和无监督方法的局限性。弱监督方法因标注数据稀缺而效果受限，而无监督方法的精度直接依赖于图像相似性度量。我们提出了一种监督式的配准模型训练方法，且无需任何标注数据。OFG通过在训练过程中使用可微分优化器细化形变预测来生成伪真实值，从而实现直接监督学习。OFG能高效优化形变预测，在不牺牲推理速度的前提下提升配准模型的性能。我们的方法在多个基准数据集和主流模型上进行了测试，显著提升了性能，为基于学习的配准模型训练提供了即插即用的解决方案。代码发布于：https://github.com/cilix-ai/on-the-fly-guidance