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[MICCAI 2024] On-the-Fly Guidance Training for Medical Image Registration. Pre-print available in link below.

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On-the-Fly Guidance (OFG)

For training medical image registration models

PWC PWC

OFG is a training framework that successfully unites learning-based methods with optimization techniques to enhance the training of learning-based registration models. OFG provides guidance with pseudo ground truth to the model by optimizing the model's output on-the-fly, which allows the model to learn from the optimization process and improve its performance.

Overall Architecture

ofg_arch

OFG is a two stage training method, integrating optimization-based methods with registration models. It optimize the model's output in training time, this process generates a pseudo label on-the-fly, which will provide supervision for the model, yielding a model with better registration performance.

Performance Benchmark

benchmark

OFG consistently improves the registration methods it is used on, and achieves state-of-the-art performance. It has better trainability than unsupervised methods while not using any manually added labels.

Registration on LPBA40

ofg_lpba

OFG provides much smoother deformation while also improving DSC of registration, combining into better overall registration performance across a wide range of modalities and datasets.

Citation

Cite our work when comparing results:

@article{ofg2024,
      title={On-the-Fly Guidance Training for Medical Image Registration}, 
      author={Yuelin Xin and Yicheng Chen and Shengxiang Ji and Kun Han and Xiaohui Xie},
      year={2024},
      eprint={2308.15216},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2308.15216}, 
}