Unsupervised 3D Registration Through Optimization-Guided Cyclical Self-training-Reference-Cited by-同舟云学术

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Unsupervised 3D Registration Through Optimization-Guided Cyclical Self-training

Published:2023 Issue: Volume: Page:677-687
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Bigalke Alexander,Hansen Lasse,Mok Tony C. W.,Heinrich Mattias P.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43999-5_64

Reference29 articles.

1. Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)

2. Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)

3. Bigalke, A., Hansen, L., Heinrich, M.P.: Adapting the mean teacher for keypoint-based lung registration under geometric domain shifts. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VI, pp. 280–290. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16446-0_27

4. Cao, Y.H., Wu, J.: A random CNN sees objects: one inductive bias of CNN and its applications. In: Proceedings Of The AAAI Conference On Artificial Intelligence. vol. 36, pp. 194–202 (2022)

5. Castillo, R., et al.: A reference dataset for deformable image registration spatial accuracy evaluation using the copdgene study archive. Phys. Med. Bio. 58(9), 2861 (2013)

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1. Mutualreg: Mutual Learning for Unsupervised Medical Image Registration;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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