Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields. These models typically depend on the intensity-based similarity loss to obtain the learning convergence. Despite the success, such dependence is insufficient. For the deformable registration of mono-modality image, well-aligned two images not only have indistinguishable intensity differences, but also are close in the statistical distribution and the boundary areas. Considering that well-designed loss functions can facilitate a learning model into a desirable convergence, we learn a deformable registration model for T1-weighted MR images by integrating multiple image characteristics via a hybrid loss. Our method registers the OASIS dataset with high accuracy while preserving deformation smoothness.
@article{arxiv.2110.15027,
title = {Deformable Registration of Brain MR Images via a Hybrid Loss},
author = {Luyi Han and Haoran Dou and Yunzhi Huang and Pew-Thian Yap},
journal= {arXiv preprint arXiv:2110.15027},
year = {2021}
}
Comments
Ranked fifth on the brain T1w deformable registration task organized by the MICCAI 2021 Learn2Reg challenge