Abstract
Hyperparameter tuning is extremely tedious and costly in the deep learning-based deformable image registration methods. In this paper, we present our contribution to the Learn2Reg challenge and demonstrate how hyperparameter tuning can be accelerated and simplified with the proposed conditional image registration framework. We exemplify the conditional image registration framework with the deep Laplacian pyramid image registration network (cLapIRN) and apply it comprehensively to all three tasks in the challenge. Our method was ranked the first place in the Learn2Reg 2021 challenge.
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Mok, T.C.W., Chung, A.C.S. (2022). Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_23
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DOI: https://doi.org/10.1007/978-3-030-97281-3_23
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