On-the-Fly Guidance Training for Medical Image Registration
This provides a plug-and-play solution for enhancing learning-based medical image registration models, addressing data scarcity issues in the medical imaging domain, though it is incremental as it builds on existing methods.
The study tackled the problem of limited labeled data in medical image registration by introducing an On-the-Fly Guidance training framework that generates pseudo-ground truth during training, resulting in significant performance improvements across benchmark datasets without affecting inference speed.
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