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  4. Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge
 
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2022
Conference Paper
Title

Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

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.
Author(s)
Hering, Alessa
Fraunhofer-Institut für Digitale Medizin MEVIS  
Lange, Annkristin
Fraunhofer-Institut für Digitale Medizin MEVIS  
Heldmann, Stefan
Fraunhofer-Institut für Digitale Medizin MEVIS  
Häger, Stephanie
Fraunhofer-Institut für Digitale Medizin MEVIS  
Kuckertz, Sven
Fraunhofer-Institut für Digitale Medizin MEVIS  
Mainwork
Biomedical image registration, domain generalisation and out-of-distribution analysis  
Conference
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021  
Learn2Reg Challenge 2021  
DOI
10.1007/978-3-030-97281-3_21
Language
English
Fraunhofer-Institut für Digitale Medizin MEVIS  
Keyword(s)
  • Image Registration

  • Learn2Reg

  • Registration Challenge

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