pdd2.5

This pytorch implementation complements the paper "Highly accurate and memory efficientunsupervised learning-based discrete CTregistration using 2.5D displacement search" by MP Heinrich and L Hansen, which was accepted at MICCAI 2020. Feel free to ask questions, by raising issues. Please cite our work if you make use of the public implementation. The data used here is part of the training dataset for the MICCAI 2020 Learn2Reg challenge and available here: https://learn2reg.grand-challenge.org/Dataset/ (Task 3)