Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

Alessa Hering; Annkristin Lange; Stefan Heldmann; Stephanie Häger; Sven Kuckertz

Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

Alessa Hering^*, Annkristin Lange, Stefan Heldmann, Stephanie Häger, Sven Kuckertz

^*Corresponding author for this work

Institute of Mathematics and Image Computing

Fraunhofer Institute for Digital Medicine MEVIS

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science
Volume	LNIP, volume 13166
Publication date	2022
Publication status	Published - 2022

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Cite this

@inproceedings{c601a33e58e64bfba37199d61675dafd,

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 = "Alessa Hering and Annkristin Lange and Stefan Heldmann and Stephanie H{\"a}ger and Sven Kuckertz",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.",

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language = "English",

volume = "LNIP, volume 13166",

booktitle = "Lecture Notes in Computer Science",

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TY - GEN

T1 - Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

AU - Hering, Alessa

AU - Lange, Annkristin

AU - Heldmann, Stefan

AU - Häger, Stephanie

AU - Kuckertz, Sven

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - LNIP, volume 13166

BT - Lecture Notes in Computer Science

ER -

Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge

Abstract

UN SDGs

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