@inproceedings{b6b8d62f7dce47fea6339b4f52e8f65e,
title = "Unsupervised Learning of Diffeomorphic Image Registration via TransMorph",
abstract = "In this work, we propose a learning-based framework for unsupervised and end-to-end learning of diffeomorphic image registration. Specifically, the proposed network learns to produce and integrate time-dependent velocity fields in an LDDMM setting. The proposed method guarantees a diffeomorphic transformation and allows the transformation to be easily and accurately inverted. We also showed that, without explicitly imposing a diffeomorphism, the proposed network can provide a significant performance gain while preserving the spatial smoothness in the deformation. The proposed method outperforms the state-of-the-art registration methods on two widely used publicly available datasets, indicating its effectiveness for image registration. The source code of this work is available at: https://bit.ly/3EtYUFN.",
keywords = "Deep neural networks, Image registration, Transformer",
author = "Junyu Chen and Frey, {Eric C.} and Yong Du",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Workshop on Biomedical Image Registration, WBIR 2020 ; Conference date: 10-07-2022 Through 12-07-2022",
year = "2022",
doi = "10.1007/978-3-031-11203-4_11",
language = "English (US)",
isbn = "9783031112027",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "96--102",
editor = "Alessa Hering and Julia Schnabel and Miaomiao Zhang and Enzo Ferrante and Mattias Heinrich and Daniel Rueckert",
booktitle = "Biomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings",
address = "Germany",
}