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Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

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Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis (MICCAI 2021)

Abstract

In this paper, we present our contribution to the learn2reg challenge. We applied the Fraunhofer MEVIS registration library RegLib comprehensively to all 3 tasks of the challenge, where we used a classic iterative registration method with NGF distance measure, second order curvature regularizer and a multi-level optimization scheme. We show that with our proposed method robust results can be achieved throughout all tasks resulting in the fourth place overall task and the best accuracy on the lung CT registration task.

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References

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Correspondence to Alessa Hering .

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Hering, A., Lange, A., Heldmann, S., Häger, S., Kuckertz, S. (2022). Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 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_21

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  • DOI: https://doi.org/10.1007/978-3-030-97281-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97280-6

  • Online ISBN: 978-3-030-97281-3

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