POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration
<|reference_start|>POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration: This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.<|reference_end|>
@article{legouhy2024polaffini:,
    title={POLAFFINI: Efficient feature-based polyaffine initialization for
  improved non-linear image registration},
    author={Antoine Legouhy, Ross Callaghan, Hojjat Azadbakht and Hui Zhang},
    journal={Information Processing in Medical Imaging. IPMI 2023. Lecture
  Notes in Computer Science, vol 13939. Springer, Cham},
    year={2024},
    doi={10.1007/978-3-031-34048-2_47},
    archivePrefix={arXiv},
    eprint={2407.03922},
    primaryClass={cs.CV}
}