Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
530-02-001 ISMRM Abstract

RealKeyMorph: A Resolution-Agnostic Keypoint-Based Image Registration Framework for MRI

Accepted
Mina Chookhachizadeh Moghadam1, Alan Wang2, Omer Taub3, Martin R Prince 4,5, Mert Sabuncu4,6
1Department of Radiology, Weill Cornell Medicine, Cornell University, New York, United States of America
2Stanford University, Stanford, United States of America
3Weill Cornell Medicine, New York, United States of America
4Radiology, Weill Cornell Medicine, New York, United States of America
5Columbia University Vagelos College of Physicians and Surgeons, New York, United States of America
6Cornell University, Ithaca, United States of America
Presenting Author: Martin R Prince

Synopsis

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References

1. 1. Kurugol, S. et al. Motion-robust parameter estimation in abdominal diffusion-weighted MRI by simultaneous image registration and model estimation. Med Image Anal 39, (2017), doi: 10.1016/j.media.2017.04.006. [doi]
2. 2. Fu, Y. et al. Deep learning in medical image registration: A review. Physics in Medicine and Biology vol. 65 Preprint at https://doi.org/10.1088/1361-6560/ab843e (2020). [doi]
3. 3. Maintz, J. B. A. & Viergever, M. A. A survey of medical image registration. Med Image Anal 2, (1998), DOI: 10.1016/s1361-8415(01)80026-8. [doi]
4. 4. Sotiras, A., Davatzikos, C. & Paragios, N. Deformable medical image registration: A survey. IEEE Trans Med Imaging 32, (2013), DOI: 10.1109/TMI.2013.2265603. [doi]
5. 5. Billot, B. et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal 86, (2023), https://doi.org/10.1016/j.media.2023.102789. [doi]
6. 6. Xu, Z., Luo, J., Yan, J., Li, X. & Jayender, J. F3RNet: full-resolution residual registration network for deformable image registration. Int J Comput Assist Radiol Surg 16, (2021), DOI: 10.1007/s11548-021-02359-4. [doi]
7. 7. Uzunova, H., Wilms, M., Handels, H. & Ehrhardt, J. Training CNNs for image registration from few samples with model-based data augmentation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 10433 LNCS (2017).
8. 8. Lee, M. C. H., Oktay, O., Schuh, A., Schaap, M. & Glocker, B. Image-and-Spatial Transformer Networks for Structure-Guided Image Registration. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 11765 LNCS (2019).
9. 9. Eppenhof, K. A. J. & Pluim, J. P. W. Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks. IEEE Trans Med Imaging 38, (2019).
10. 10. Cao, X. et al. Deformable image registration using a cue-aware deep regression network. IEEE Trans Biomed Eng 65, (2018), doi: 10.1109/TBME.2018.2822826. [doi]
11. 11. Haskins, G., Kruger, U. & Yan, P. Deep learning in medical image registration: a survey. Mach Vis Appl 31, (2020), https://doi.org/10.48550/arXiv.1903.02026. [doi]
12. 12. 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, (2019), DOI: 10.1109/TMI.2019.2897538. [doi]
13. 13. Yu, E. M., Wang, A. Q., Dalca, A. V. & Sabuncu, M. R. KeyMorph: Robust Multi-modal Affine Registration via Unsupervised Keypoint Detection. in Proceedings of Machine Learning Research vol. 172 (2022).
14. 14. Wang, A. Q., Yu, E. M., Dalca, A. V. & Sabuncu, M. R. A robust and interpretable deep learning framework for multi-modal registration via keypoints. Med Image Anal 90, (2023).
15. 15. Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, (2004).
16. 16. Wood, S. N. Thin plate regression splines. Journal of the Royal Statistical Society. Series B: Statistical Methodology vol. 65 Preprint at https://doi.org/10.1111/1467-9868.00374 (2003). [doi]
17. 17. Zhu, C. X. H. J. D. B. M. A. D. R. K. T. H. Y. N. H. Y. W. A. S. F. E. W. A. G. P. J. M. C. D. S. M. C. M. M. S. M. R. P. A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression. Biomedicines 2024, DOI: 10.3390/biomedicines12051133. [doi]
18. 18. Sharbatdaran, A. et al. Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease. Tomography 8, (2022), DOI: 10.3390/tomography8040152. [doi]
19. 19. Pérez-García, F., Sparks, R. & Ourselin, S. TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput Methods Programs Biomed 208, (2021).
20. 20. Olaf Ronneberger, P. F. T. B. U-Net: Convolutional Networks for Biomedical Image Segmentation. (2015).

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