Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge

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

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.