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

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.
OriginalspracheEnglisch
TitelInternational Conference on Medical Image Computing and Computer-Assisted Intervention : MICCAI 2020: Segmentation, Classification and Registration of Multi-modality Medical Imaging Data
Erscheinungsdatum13.03.2021
PublikationsstatusVeröffentlicht - 13.03.2021

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