Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
<|reference_start|>Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks: Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology. Currently, unsupervised learning-based registration techniques primarily extract features at the image level, potentially limiting their efficacy. This paper proposes a novel unsupervised learning-based fully convolutional network (FCN) framework for fast diffeomorphic image registration, emphasizing feature acquisition at the image patch level. Furthermore, a novel differential operator is introduced and integrated into the FCN architecture for parameter learning. Experiments are conducted on three distinct T1-weighted magnetic resonance imaging (T1w MRI) datasets. Comparative analyses with three state-of-the-art diffeomorphic image registration approaches including a typical conventional registration algorithm and two representative unsupervised learning-based methods, reveal that the proposed method exhibits superior performance in both registration accuracy and topology preservation.<|reference_end|>
@article{wu2024fast,
    title={Fast Diffeomorphic Image Registration using Patch based Fully
  Convolutional Networks},
    author={Jiong Wu, Shuang Zhou, Li Lin, Xin Wang and Wenxue Tan},
    journal={arXiv preprint arXiv:2404.04244},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04244},
    primaryClass={cs.CV}
}