NCF: Neural Correspondence Field for Medical Image Registration

Deformable image registration is a fundamental task in medical image processing. Traditional optimization-based methods often struggle with accuracy in dealing with complex deformation. Recently, learning-based methods have achieved good performance on public datasets, but the scarcity of medical image data makes it challenging to build a generalizable model to handle diverse real-world scenarios. To address this, we propose a training-data-free learning-based method, Neural Correspondence Field (NCF), which can learn from just one data pair. Our approach employs a compact neural network to model the correspondence field and optimize model parameters for each individual image pair. Consequently, each pair has a unique set of network weights. Notably, our model is highly efficient, utilizing only 0.06 million parameters. Evaluation results showed that the proposed method achieved superior performance on a public Lung CT dataset and outperformed a traditional method on a head and neck dataset, demonstrating both its effectiveness and efficiency.

@article{zhou2025_2503.00760,
  title={ NCF: Neural Correspondence Field for Medical Image Registration },
  author={ Lei Zhou and Nimu Yuan and Katjana Ehrlich and Jinyi Qi },
  journal={arXiv preprint arXiv:2503.00760},
  year={ 2025 }
}