Variable fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge

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

Variable fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge

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

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Mathematische Methoden der Bildverarbeitung

Fraunhofer MEVIS

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.

Originalsprache	Englisch
Titel	Lecture Notes in Computer Science
Band	LNIP, volume 12587
Erscheinungsdatum	13.03.2021
Publikationsstatus	Veröffentlicht - 13.03.2021

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title = "Variable fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge",

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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AU - Häger, Stephanie

AU - Heldmann, Stefan

AU - Hering, Alessa

AU - Kuckertz, Sven

AU - Lange, Annkristin

PY - 2021/3/13

Y1 - 2021/3/13

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - LNIP, volume 12587

BT - Lecture Notes in Computer Science

ER -

Variable fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge

Abstract

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