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.
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.