Abstract
The Learn2Reg challenge poses four very different tasks with varying difficulty for image registration algorithms. In this short paper, we describe our choices for two state-of-the-art discrete 3D registration methods that enable fast and accurate estimation of large deformations without expert supervision during training. Both approaches primarily focus on the use of contrast-invariant features with dense displacement evaluation and were ranked among the top three of all challenge contestants, yielding two first places and three second places for the four sub-tasks.
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Hansen, L., Heinrich, M.P. (2021). Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science(), vol 12587. Springer, Cham. https://doi.org/10.1007/978-3-030-71827-5_8
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DOI: https://doi.org/10.1007/978-3-030-71827-5_8
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