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Coarse-to-fine medical image registration with landmarks and deformable networks

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Abstract

Medical image registration is a critical task in medical image processing. However, most deep learning-based registration methods primarily rely on a single network to directly predict the deformation field, which can result in challenges when accurately matching certain complex imaging regions. Moreover, the pervasive issue of insufficient training data in medical image registration further limits improvements in model performance. To address these challenges, we propose a novel coarse-to-fine image registration architecture (CFIR) consisting of a landmark-based registration network (LRN) and a deformable registration network (DRN). LRN first extracts and aligns contours and key anatomical regions from the images, achieving coarse registration. Subsequently, DRN refines the alignment by concentrating on finer details that were not adequately addressed during the coarse registration stage, ensuring precise alignment of local features between the images to be registered. During the training process, we employ segmentation and recombination to augment the training data. The performance of CFIR is rigorously evaluated on brain MRI and abdominal CT registration tasks, demonstrating superior registration accuracy compared to several existing CNN-based and Transformer-based methods. Our method provides a new paradigm for medical image registration.

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No datasets were generated or analysed during the current study.

References

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

    Article  MATH  Google Scholar 

  2. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang M-C, Christensen GE, Collins DL, Gee J, Hellier P et al (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3):786–802

    Article  Google Scholar 

  3. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044

    Article  MATH  Google Scholar 

  4. Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, Fox NC, Ourselin S (2010) Fast free-form deformation using graphics processing units. Comput Methods Progr Biomed 98(3):278–284

    Article  Google Scholar 

  5. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2019) Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging 38(8):1788–1800

    Article  Google Scholar 

  6. Jaderberg M, Simonyan K, Zisserman A et al. (2015) Spatial transformer networks. Adv Neural Inf Process Syst vol 28

  7. Vaswani A (2017) Attention is all you need. Adv Neural Inf Process Syst

  8. Meng M, Bi L, Feng D, Kim J (2022) Non-iterative coarse-to-fine registration based on single-pass deep cumulative learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention pp 88–97. Springer

  9. Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y (2022) Transmorph: transformer for unsupervised medical image registration. Med Image Anal 82:102615

    Article  Google Scholar 

  10. Ma T, Dai X, Zhang S, Wen Y (2023) Pivit: large deformation image registration with pyramid-iterative vision transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention pp 602–612. Springer

  11. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision pp 10012–10022

  12. Chen Y, Hu X, Lu T, Zou L, Liao X (2025) A multi-scale large kernel attention with u-net for medical image registration. J Supercomput 81(1):70

    Article  MATH  Google Scholar 

  13. Chen Z, Zheng Y, Gee JC (2023) Transmatch: a transformer-based multilevel dual-stream feature matching network for unsupervised deformable image registration. IEEE Trans Med Imaging 43(1):15–27

    Article  Google Scholar 

  14. Meng M, Bi L, Fulham M, Feng DD, Kim J (2022) Enhancing medical image registration via appearance adjustment networks. NeuroImage 259:119444

    Article  Google Scholar 

  15. Shanker R, Sankesara H, Nagar S, Bhattacharya M (2023) Respnet: resource-efficient and structure-preserving network for deformable image registration. J Supercomput 79(5):4713–4736

    Article  MATH  Google Scholar 

  16. Mok TC, Chung AC (2020) Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: Medical Iage Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp 211–221. Springer

  17. Liu C, He K, Xu D, Shi H (2025) MDH-Net: advancing 3D brain MRI registration with multi-stage transformer and dual-stream feature refinement hybrid network. J Supercomput 81(1):19

    Article  MATH  Google Scholar 

  18. Zhao S, Dong Y, Chang EI, Xu Y et al. (2019) Recursive cascaded networks for unsupervised medical image registration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10600–10610

  19. Zhang L, Zhou L, Li R, Wang X, Han B, Liao H (2021) Cascaded feature warping network for unsupervised medical image registration. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp 913–916. IEEE

  20. Zhao S, Lau T, Luo J, Eric I, Chang C, Xu Y (2019) Unsupervised 3D end-to-end medical image registration with volume tweening network. IEEE J Biomed Health Inform 24(5):1394–1404

    Article  Google Scholar 

  21. Huang W, Yang H, Liu X, Li C, Zhang I, Wang R, Zheng H, Wang S (2021) A coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint. IEEE Trans Med Imaging 40(10):2589–2599

    Article  MATH  Google Scholar 

  22. Meng M, Bi L, Fulham M, Feng D, Kim J (2023) Non-iterative coarse-to-fine transformer networks for joint affine and deformable image registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 750–760. Springer

  23. Bajcsy R, Kovačič S (1989) Multiresolution elastic matching. Comput Vis Graph Image Process 46(1):1–21

    Article  Google Scholar 

  24. Beg MF, Miller MI, Trouvé A, Younes L (2005) Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vis 61:139–157

    Article  MATH  Google Scholar 

  25. Dalca AV, Bobu A, Rost NS, Golland P (2016) Patch-based discrete registration of clinical brain images. In: Patch-Based Techniques in Medical Imaging: Second International Workshop, Patch-MI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings 2, pp 60–67. Springer

  26. Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95–113

    Article  MATH  Google Scholar 

  27. Yeo BT, Sabuncu MR, Vercauteren T, Holt DJ, Amunts K, Zilles K, Golland P, Fischl B (2010) Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex. IEEE Trans Med Imaging 29(7):1424–1441

    Article  Google Scholar 

  28. Glocker B, Komodakis N, Tziritas G, Navab N, Paragios N (2008) Dense image registration through MRFs and efficient linear programming. Med Image Anal 12(6):731–741

    Article  MATH  Google Scholar 

  29. Thirion J-P (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3):243–260

    Article  MATH  Google Scholar 

  30. Zhang M, Liao R, Dalca AV, Turk EA, Luo J, Grant PE, Golland P (2017) Frequency diffeomorphisms for efficient image registration. In: Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25–30, 2017, Proceedings 25, pp 559–570. Springer

  31. Shen D, Davatzikos C (2002) Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21(11):1421–1439

    Article  MATH  Google Scholar 

  32. Wulff J, Black MJ (2015) Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 120–130

  33. Hart GL, Zach C, Niethammer M (2009) An optimal control approach for deformable registration. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp 9–16. IEEE

  34. Chen Z, Jin H, Lin Z, Cohen S, Wu Y (2013) Large displacement optical flow from nearest neighbor fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2443–2450

  35. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1):61–72

    Article  MATH  Google Scholar 

  36. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721

    Article  MATH  Google Scholar 

  37. Pennec X, Fillard P, Ayache N (2006) A Riemannian framework for tensor computing. Int J Comput Vis 66:41–66

    Article  MATH  Google Scholar 

  38. Kim B, Kim J, Lee J-G, Kim DH, Park SH, Ye JC (2019) Unsupervised deformable image registration using cycle-consistent CNN. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd International Cnference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22, pp 166–174. Springer

  39. Cao X, Yang J, Zhang J, Nie D, Kim M, Wang Q, Shen D (2017) Deformable image registration based on similarity-steered CNN regression. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11–13, 2017, Proceedings, Part I 20, pp 300–308. Springer

  40. De Vos BD, Berendsen FF, Viergever MA, Staring M, Išgum I (2017) End-to-end unsupervised deformable image registration with a convolutional neural network. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, pp 204–212. Springer

  41. Zhu Y, Lu S (2022) Swin-voxelmorph: a symmetric unsupervised learning model for deformable medical image registration using swin transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 78–87. Springer

  42. Wang H, Ni D, Wang Y (2023) Modet: learning deformable image registration via motion decomposition transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 740–749. Springer

  43. Yoo I, Hildebrand DG, Tobin WF, Lee W-CA, Jeong W-K (2017) ssEMnet: Serial-section electron microscopy image registration using a spatial transformer network with learned features. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, pp 249–257. Springer

  44. Bhalodia R, Elhabian S, Kavan L, Whitaker R (2021) Leveraging unsupervised image registration for discovery of landmark shape descriptor. Med Image Anal 73:102157

    Article  Google Scholar 

  45. Shu Y, Wang H, Xiao B, Bi X, Li W (2021) Medical image registration based on uncoupled learning and accumulative enhancement. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 3–13. Springer

  46. Koch G, Zemel R, Salakhutdinov R, et al. (2015) Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol 2, pp 1–30. Lille

  47. Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, Szeskin A, Jacobs C, Mamani GEH, Chartrand G et al (2023) The liver tumor segmentation benchmark (lits). Med Image Anal 84:102680

    Article  Google Scholar 

  48. Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265

    Article  Google Scholar 

  49. Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008) Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3):1064–1080

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Natural Science Foundation of Heilongjiang Province under Grant No. PL2024F022.

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Authors and Affiliations

  1. School of Computer Science, Dalian University of Technology, Dalian, 116024, China

    Zhipeng Cao, Nianmin Yao, Linqi Meng & Jingyi Fang

  2. School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China

    Jian Zhao

  3. Ningbo Institute of Dalian University of Technology, Ningbo, 315016, China

    Nianmin Yao & Jian Zhao

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Contributions

ZC: Writing—review & editing, writing—original draft, visualization, validation, supervision, software, methodology. NY: Project administration, investigation, data curation. LM: Writing—original draft. JF: Writing—original draft. JZ: Validation, supervision, software.

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Correspondence to Nianmin Yao.

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Cao, Z., Yao, N., Meng, L. et al. Coarse-to-fine medical image registration with landmarks and deformable networks. J Supercomput 81, 480 (2025). https://doi.org/10.1007/s11227-025-07000-8

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