Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge

S. Häger, S. Heldmann, A. Hering, S. Kuckertz and A. Lange

Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020 2021;12587:74-79.


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.