Abstract
In this paper, we present our contribution to the learn2reg challenge. We applied the Fraunhofer MEVIS registration library RegLib comprehensively to all 4 tasks of the challenge. For tasks 1–3, we used a classic iterative registration method with NGF distance measure, second order curvature regularizer, and a multi-level optimization scheme. For task 4, a deep learning approach with a weakly supervised trained U-Net was applied using the same cost function as in the iterative approach.
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Häger, S., Heldmann, S., Hering, A., Kuckertz, S., Lange, A. (2021). Variable Fraunhofer MEVIS RegLib Comprehensively Applied to 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_9
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DOI: https://doi.org/10.1007/978-3-030-71827-5_9
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