For training medical image registration models
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.
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.
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.
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.
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},
}