For training medical image registration models
OFG is a general training framework that provides an alternative to weakly-supervised and unsupervised training for image registration models. By iteratively optimizing the prediction result of the trained registration model on-the-fly, OFG introduces pseudo ground truth to an unsupervised training process. This supervision provides more direct guidance towards model training compared with unsupervised methods.
![ofg](https://private-user-images.githubusercontent.com/89094576/285325852-409cde65-02bc-4332-b5eb-24ac5099ce4f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTg0MTA1MzUsIm5iZiI6MTcxODQxMDIzNSwicGF0aCI6Ii84OTA5NDU3Ni8yODUzMjU4NTItNDA5Y2RlNjUtMDJiYy00MzMyLWI1ZWItMjRhYzUwOTljZTRmLnBuZz9YLUFtei1BbGdvcml0aG09QVdTNC1ITUFDLVNIQTI1NiZYLUFtei1DcmVkZW50aWFsPUFLSUFWQ09EWUxTQTUzUFFLNFpBJTJGMjAyNDA2MTUlMkZ1cy1lYXN0LTElMkZzMyUyRmF3czRfcmVxdWVzdCZYLUFtei1EYXRlPTIwMjQwNjE1VDAwMTAzNVomWC1BbXotRXhwaXJlcz0zMDAmWC1BbXotU2lnbmF0dXJlPWU4ZDI5M2MyYjQ2MTg5MTRjMTc4NGJlMDQyMTAxYmNkZmEwYzg1NjAzNmJjNmVlNzE3YzJhZGRiNGE2ZGMxNTImWC1BbXotU2lnbmVkSGVhZGVycz1ob3N0JmFjdG9yX2lkPTAma2V5X2lkPTAmcmVwb19pZD0wIn0.fctPa53hqmiOechBzPfxRCJI8GZA771JH5Mfxzm1-3Q)
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.
![hero](https://private-user-images.githubusercontent.com/89094576/285325879-9fe81ae2-a03d-4036-bbb7-af0b32595dab.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.zbt8DlHcSX7euu4D9tm6DZlqENO6ANguVgrr4bcCwCk)
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.
Cite our work when comparing results:
@article{ofg2023,
title={On-the-Fly Guidance Training for Medical Image Registration},
author={Yicheng Chen and Shengxiang Ji and Yuelin Xin and Kun Han and Xiaohui Xie},
year={2023},
eprint={2308.15216},
archivePrefix={arXiv},
primaryClass={cs.CV}
}