Abstract
This study introduces a novel On-the-Fly Guidance (OFG) training framework for enhancing existing learning-based image registration models, addressing the limitations of weakly-supervised and unsupervised methods. Weakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy. Our method proposes a supervised fashion for training registration models, without the need for any labeled data. OFG generates pseudo-ground truth during training by refining deformation predictions with a differentiable optimizer, enabling direct supervised learning. OFG optimizes deformation predictions efficiently, improving the performance of registration models without sacrificing inference speed. Our method is tested across several benchmark datasets and leading models, it significantly enhanced performance, providing a plug-and-play solution for training learning-based registration models. Code available at: https://github.com/cilix-ai/on-the-fly-guidance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage p. 95-113 (Oct 2007)
AV, D., A, B., NS, R., P, G.: Patch-based discrete registration of clinical brain images. Patch Based Tech Med Imaging (2016) (2016)
Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 12(1), 26–41 (2008), special Issue on The Third International Workshop on Biomedical Image Registration - WBIR 2006
Bajcsy, R., Kovačič, S.: Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing 46(1), 1–21 (1989)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: A learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging 38(8), 1788–1800 (aug 2019)
Bigalke, A., Hansen, L., Mok, T.C.W., Heinrich, M.P.: Unsupervised 3d registration through optimization-guided cyclical self-training. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. pp. 677–687. Springer Nature Switzerland, Cham (2023)
C, C., L, W., LD, S., JG, C., MI, M., JT, R.: Large deformation diffeomorphic metric mapping registration of reconstructed 3d histological section images and in vivo mr images. Front Hum Neurosci (2010)
Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: Transformer for unsupervised medical image registration. Medical Image Analysis 82, 102615 (nov 2022)
Chen, J., He, Y., Frey, E.C., Li, Y., Du, Y.: Vit-v-net: Vision transformer for unsupervised volumetric medical image registration (2021)
Chen, Q., Li, Z., Lui, L.M.: A learning framework for diffeomorphic image registration based on quasi-conformal geometry. CoRR abs/2110.10580 (2021)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Medical Image Analysis 12(6), 731–741 (2008), special issue on information processing in medical imaging 2007
Hering, A., Hansen, L., Mok, T.C.W., Chung, A.C.S., Siebert, H., Häger, S., Lange, A., Kuckertz, S., Heldmann, S., Shao, W., Vesal, S., Rusu, M., Sonn, G., Estienne, T., Vakalopoulou, M., Han, L., Huang, Y., Yap, P.T., Brudfors, M., Balbastre, Y., Joutard, S., Modat, M., Lifshitz, G., Raviv, D., Lv, J., Li, Q., Jaouen, V., Visvikis, D., Fourcade, C., Rubeaux, M., Pan, W., Xu, Z., Jian, B., De Benetti, F., Wodzinski, M., Gunnarsson, N., Sjölund, J., Grzech, D., Qiu, H., Li, Z., Thorley, A., Duan, J., Großbröhmer, C., Hoopes, A., Reinertsen, I., Xiao, Y., Landman, B., Huo, Y., Murphy, K., Lessmann, N., van Ginneken, B., Dalca, A.V., Heinrich, M.P.: Learn2reg: Comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Transactions on Medical Imaging 42(3), 697–712 (2023). https://doi.org/10.1109/TMI.2022.3213983
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks (2016)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2017)
Liu, R., Li, Z., Fan, X., Zhao, C., Huang, H., Luo, Z.: Learning deformable image registration from optimization: Perspective, modules, bilevel training and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(11), 7688–7704 (2022)
Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using free-form deformations with a local rigidity constraint. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. pp. 639–646. Springer Berlin Heidelberg, Berlin, Heidelberg (2004)
London, I.C.: Information extraction from images (2023), https://brain-development.org/ixi-dataset/
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (09 2007)
Centre for Medical Image Computing, University College London, U.: Niftyreg (2023), http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg
Nazib, A., Fookes, C., Perrin, D.: A comparative analysis of registration tools: Traditional vs deep learning approach on high resolution tissue cleared data (2018)
Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21(11), 1421–1439 (2002)
Shen, Z., Han, X., Xu, Z., Niethammer, M.: Networks for Joint Affine and Non-parametric Image Registration. arXiv e-prints arXiv:1903.08811 (Mar 2019)
Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., Išgum, I., Staring, M.: Nonrigid image registration using multi-scale 3d convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2017. pp. 232–239. Springer International Publishing, Cham (2017)
University of Southern California, L.o.N.I.: Loni probabilistic brain atlas (lpba40) (2023), https://loni.usc.edu/research/atlases
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Medical Image Analysis 52, 128–143 (feb 2019)
Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: Fast predictive image registration - a deep learning approach (2017)
Zhang, Y., Pei, Y., Zha, H.: Learning dual transformer network for diffeomorphic registration. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. pp. 129–138. Springer International Publishing, Cham (2021)
Zou, J., Gao, B., Song, Y., Qin, J.: A review of deep learning-based deformable medical image registration. Frontiers in Oncology 12 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xin, Y., Chen, Y., Ji, S., Han, K., Xie, X. (2024). On-the-Fly Guidance Training for Medical Image Registration. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-72069-7_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72068-0
Online ISBN: 978-3-031-72069-7
eBook Packages: Computer ScienceComputer Science (R0)
