Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

A multi-scale large kernel attention with U-Net for medical image registration

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Deformable image registration minimizes the discrepancy between moving and fixed images by establishing linear and nonlinear spatial correspondences. It plays a crucial role in surgical navigation, image fusion and disease analysis. Its challenge lies in the large number of deformed parameters and the uncertainty of acquisition conditions. Benefiting from the powerful ability to capture hierarchical features and spatial relationships of convolutional neural networks, the medical image registration task has made great progress. Nowadays, the long-range relationship modeling and adaptive selection of self-attention show great potential and have also attracted much attention from researchers. Inspired by this, we propose a new method called Multi-scale Large Kernel Attention UNet (MLKA-Net), which combines a large kernel convolution with the attention mechanism using a multi-scale strategy, and uses a correction module to fine-tune the deformation field to achieve high-accuracy registration. Specifically, we first propose a multi-scale large kernel attention mechanism (MLKA), which generates attention maps by aggregating information from convolution kernels at different scales to improve local feature modeling capabilities of attention. Furthermore, we employ large kernel dilation convolution in proposed attention to construct sufficiently long-range relationships, while keeping lower number of parameters. Finally, to further improve local accuracy of the registration, we design an additional correction module and unsupervised framework to fine-tune the deformation field to solve the issue of original information loss in multilayer networks. Our method is compared qualitatively and quantitatively with 24 representative and advanced methods on the 3 public available 3D datasets from IXI database, LPBA40 dataset and OASIS database, respectively. The experiments demonstrate the excellent performance of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

All datasets used in this study are publicly available. The IXI dataset is available at Brain Development (http://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1, accessed on 27 January 2024), and the 3D OASIS MRI dataset and 2D OASIS MRI dataset are available at OASIS database (https://www.oasis-brains.org, accessed on 27 January 2024). The LPBA40 dataset is available at (https://www.loni.usc.edu/research/atlas_downloads, accessed on 27 January 2024),

Notes

  1. https://learn2reg.grand-challenge.org/.

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  Google Scholar 

  2. Faisal Beg Mirza, Miller Michael I, Alain Trouvé, Laurent Younes (2005) Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comp Vision 61:139–157

    Article  Google Scholar 

  3. Heinrich Mattias P, Oskar Maier, Heinz Handels (2015) Multi-modal multi-atlas segmentation using discrete optimisation and self-similarities. VISCERAL Challenge@ISBI. 1390:27

    Google Scholar 

  4. Marc Modat, Ridgway Gerard R, Taylor Zeike A, Manja Lehmann, Josephine Barnes, Hawkes David J, Fox Nick C, Sébastien Ourselin (2010) Fast free-form deformation using graphics processing units. Comp Method Programs Biomedicine 98(3):278–284

    Article  Google Scholar 

  5. Balakrishnan Guha, Zhao Amy, Sabuncu Mert R, Guttag John, Dalca Adrian V (2019) Voxelmorph: A learning framework for deformable medical image registration. IEEE Trans Med Imaging 38(8):1788–1800

    Article  Google Scholar 

  6. Chen Junyu, Frey Eric C, He Yufan, Segars William P, Li Ye, Yong Du (2022) Transmorph: Transformer for unsupervised medical image registration. Med Image Anal 82:102615

    Article  Google Scholar 

  7. Xi Jia, Joseph Bartlett, Zhang Tianyang Lu, Wenqi Qiu Zhaowen, Jinming Duan (2022) U-net vs transformer: Is u-net outdated in medical image registration? In: Lian Chunfeng, Cao Xiaohuan, Rekik Islem, Xuanang Xu, Cui Zhiming (eds) Machine Learning Medical Imaging. Cham. Springer Nature Switzerland, pp 151–160

    Google Scholar 

  8. Kim B, Kim DH, Park SH, Kim J, Lee JG, Ye JC (2021) CycleMorph: cycle consistent unsupervised deformable image registration. Med Image Anal 71:102036

    Article  Google Scholar 

  9. Shi Jiacheng, He Yuting, Kong Youyong, Coatrieux Jean-Louis, Shu Huazhong, Yang Guanyu, Li Shuo (2022) Xmorpher: Full transformer for deformable medical image registration via cross attention. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, and Shuo Li, (eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 217–226, Cham. Springer Nature Switzerland

  10. Hessam Sokooti, De Vos Bob, Floris Berendsen, Lelieveldt Boudewijn PF, Ivana Išgum, Marius Staring (2017) Nonrigid image registration using multi-scale 3d convolutional neural networks. Springer, Cham

    Google Scholar 

  11. Yutong Xie, Jianpeng Zhang, Chunhua Shen, Yong Xia (2021) Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation. In: de Bruijne Marleen, Cattin Philippe C, Cotin Stéphane, Padoy Nicolas, Speidel Stefanie, Zheng Yefeng, Essert Caroline (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. Cham. Springer International Publishing, pp 171–180

    Google Scholar 

  12. Yang Xiao, Kwitt Roland, Styner Martin, Niethammer Marc (2017) Quicksilver: Fast predictive image registration – a deep learning approach. Neuroimage 158:378–396

    Article  Google Scholar 

  13. Jaderberg Max, Simonyan Karen, Zisserman Andrew, Kavukcuoglu Koray (2015) Spatial transformer networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2, NIPS’15, page 2017–2025, Cambridge, MA, USA. MIT Press

  14. Olaf Ronneberger, Philipp Fischer, Thomas Brox (2015) U-net: Convolutional networks for biomedical image segmentation. In: Navab Nassir, Hornegger Joachim, Wells William M, Frangi Alejandro F (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Cham. Springer International Publishing, pp 234–241

    Google Scholar 

  15. Dosovitskiy Alexey, Beyer Lucas, Kolesnikov Alexander, Weissenborn Dirk, Zhai Xiaohua, Unterthiner Thomas, Dehghani Mostafa, Minderer Matthias, Heigold Georg, Gelly Sylvain, Uszkoreit Jakob, Houlsby Neil (2021) An image is worth 16x16 words: Transformers for image recognition at scale

  16. Yungeng Zhang, Yuru Pei, Hongbin Zha (2021) Learning dual transformer network for diffeomorphic registration. In: de Bruijne Marleen, Cattin Philippe C, Cotin Stéphane, Padoy Nicolas, Speidel Stefanie, Zheng Yefeng, Essert Caroline (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021–24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part IV, vol 12904. Lecture Notes in Computer Science. Springer, pp 129–138

    Google Scholar 

  17. Chen Xuxin, Wang Ximin, Zhang Ke, Fung Kar-Ming, Thai Theresa C, Moore Kathleen, Mannel Robert S, Liu Hong, Zheng Bin, Qiu Yuchen (2022) Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal 79:102444

    Article  Google Scholar 

  18. He Kelei, Gan Chen, Li Zhuoyuan, Rekik Islem, Yin Zihao, Ji Wen, Gao Yang, Wang Qian, Zhang Junfeng, Shen Dinggang (2023) Transformers in medical image analysis. Intell Medicine 3(1):59–78

    Article  Google Scholar 

  19. Shen Dinggang, Davatzikos C (2001) Hammer: hierarchical attribute matching mechanism for elastic registration. In Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 29–36

  20. Xavier Pennec, Pascal Cachier, Nicholas Ayache (1999) Understanding the “demon’s algorithm’’: 3d non-rigid registration by gradient descent. In: Taylor Chris, Colchester Alain (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI’99, 597–605, Berlin, Heidelberg. Springer, Berlin Heidelberg

    Google Scholar 

  21. Rueckert D, Sonoda LI, Hayes C, Hill DLG, 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  Google Scholar 

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

    Article  Google Scholar 

  23. Vercauteren Tom, Pennec Xavier, Perchant Aymeric, Ayache Nicholas (2009) Diffeomorphic demons: Efficient non-parametric image registration. Neuroimage 45:S61–S72

    Article  Google Scholar 

  24. Shun Miao, Jane Wang Z, Rui Liao (2016) A cnn regression approach for real-time 2d/3d registration. IEEE Transa Medical Imaging 35(5):1352–1363

    Article  Google Scholar 

  25. de Vos Bob D, Berendsen Floris F, Viergever Max A, Staring Marius, Išgum Ivana (2017) End-to-end unsupervised deformable image registration with a convolutional neural network. volume 10553 LNCS of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 204–212. Springer Verlag

  26. Dongyang Kuang, Tanya Schmah (2019) Faim - a convnet method for unsupervised 3d medical image registration. In: Suk Heung-Il, Liu Mingxia, Yan Pingkun, Lian Chunfeng (eds) Machine Learning in Medical Imaging. Cham. Springer International Publishing, pp 646–654

    Google Scholar 

  27. Zhao Shengyu, et al., (2019) Recursive cascaded networks for unsupervised medical image registration. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 10599–10609

  28. Boah Kim, Hwan Kim Dong, Ho Park Seong, Jieun Kim, June-Goo Lee, Chul Ye Jong (2021) Cyclemorph: Cycle consistent unsupervised deformable image registration. Med Image Anal 71:102036

    Article  Google Scholar 

  29. Xiaojun Hu, Miao Kang, Weilin Huang, Scott Matthew R, Roland Wiest, Mauricio Reyes (2019) Dual-stream pyramid registration network. In: Shen Dinggang, Liu Tianming, Peters Terry M, Staib Lawrence H, Essert Caroline, Zhou Sean, Yap Pew-Thian, Khan Ali (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2019. Cham. Springer International Publishing, pp 382–390

    Google Scholar 

  30. Qian Lijun, Zhou Qing, Cao Xiaohuan, Shen Wenjun, Suo Shiteng, Ma Shanshan, Guoxiang Qu, Gong Xuhua, Yan Yunqi, Jianrong Xu, Jiang Luan (2021) A cascade-network framework for integrated registration of liver dce-mr images. Comput Med Imaging Graph 89:101887

    Article  Google Scholar 

  31. Zhao Yao et al (2023) A transformer-based hierarchical registration framework for multimodality deformable image registration. Computerized Medical Imaging Graphics 108:102286

    Article  Google Scholar 

  32. Chen Junyu, He Yufan, Frey Eric C., Li Ye, Du Yong (2021) Vit-v-net: Vision transformer for unsupervised volumetric medical image registration. arXiv preprint http://arxiv.org/abs/2104.06468arXiv:2104.06468

  33. Ze Liu, Yutong Lin, Cao Yue Hu, Han Wei Yixuan, Zheng Zhang, Stephen Lin, Baining Guo (2021) Swin transformer: Hierarchical vision transformer using shifted windows. IEEE/CVF Int Conf Computer Vision (ICCV) 2021:9992–10002

    Google Scholar 

  34. Fan Jingfan, Cao Xiaohuan, Wang Qian, Yap Pew-Thian, Shen Dinggang (2019) Adversarial learning for mono- or multi-modal registration. Med Image Anal 58:101545

    Article  Google Scholar 

  35. Chitchaya Suwanraksa, Jidapa Bridhikitti, Thiansin Liamsuwan, Sitthichok Chaichulee (2023) Cbct-to-ct translation using registration-based generative adversarial networks in patients with head and neck cancer. Cancers. 15(7):2017

    Article  Google Scholar 

  36. Kim Boah, Han Inhwa, Ye Jong Chul (2022) Diffusemorph: Unsupervised deformable image registration using diffusion model. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner, (eds.), 347–364, Cham. Springer Nature Switzerland

  37. Cai Linqin, Fang Haodu, Li Zhiqing (2023) Pre-trained multilevel fuse network based on vision-conditioned reasoning and bilinear attentions for medical image visual question answering. J Supercomput 79(12):13696–13723

    Article  Google Scholar 

  38. La Salvia Marco, Torti Emanuele, Marenzi Elisa, Danese Giovanni, Leporati Francesco (2024) Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging. J Supercomput 80(11):16368–16392

    Article  Google Scholar 

  39. Liu Yu, Ao Yongcai (August 2024) Deformable attention mechanism-based YOLOv7 structure for lung nodule detection. The Journal of Supercomputing. 1-20

  40. Rane Chinmay, Mehrotra Raj, Bhattacharyya Shubham, Sharma Mukta, Bhattacharya Mahua (2021) A novel attention fusion network-based framework to ensemble the predictions of CNNs for lymph node metastasis detection. J Supercomput 77(4):4201–4220

    Article  Google Scholar 

  41. Shuai L, Guo ZH, Zhang P, Wan J, Pu X, Wang ZL (2020) Stretchable, self-healing, conductive hydrogel fibers for strain sensing and triboelectric energy-harvesting smart textiles. Nano Energy 78:105389

    Article  Google Scholar 

  42. Vaswani Ashish et al (2017) Attention is all you need. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems, vol 30. Curran Associates Inc

    Google Scholar 

  43. Dalca Adrian V, Balakrishnan Guha, Guttag John, Sabuncu Mert R (2019) Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med Image Anal 57:226–236

    Article  Google Scholar 

  44. Vincent Arsigny, Olivier Commowick, Xavier Pennec, Nicholas Ayache (2006) A log-euclidean framework for statistics on diffeomorphisms. Medical image computing and computer-assisted intervention : MICCAI. Int Conf Medical Image Comp Comp-Assist Interv 1:924–31

    Google Scholar 

  45. Shattuck David W, Mirza Mubeena, Adisetiyo Vitria, Hojatkashani Cornelius, Salamon Georges, Narr Katherine L, Poldrack Russell A, Bilder Robert M, Toga Arthur W (2008) Construction of a 3d probabilistic atlas of human cortical structures. Neuroimage 39(3):1064–1080

    Article  Google Scholar 

  46. Marcus Daniel S, Wang Tracy H, Parker Jamie, Csernansky John G, Morris John C, Buckner Randy L (2007) Open access series of imaging studies (oasis): Cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19(9):1498–1507

    Article  Google Scholar 

  47. Alessa Hering, Lasse Hansen, Mok Tony CW, Chung Albert CS, Hanna Siebert, Stephanie Hager, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu et al (2023) Geoffrey Sonn. Learn2reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Transa Medical Imaging 3:697–712

    Google Scholar 

  48. Dalca Adrian V, Balakrishnan Guha, Guttag John, Sabuncu Mert R (2018) Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration, 729–738. Springer International Publishing

  49. Lee Raymond Dice (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  50. Qiu Huaqi, Qin Chen, Schuh Andreas, Hammernik Kerstin, Rueckert Daniel (2021) Learning diffeomorphic and modality-invariant registration using b-splines. In Medical Imaging with Deep Learning

  51. Wang Wenhai, Xie Enze, Li Xiang, Fan Deng-Ping, Song Kaitao, Liang Ding, Tong Lu, Luo Ping, Shao Ling (2022) Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media 8(3):415–424

    Article  Google Scholar 

  52. Zhou Hong-Yu, Guo Jiansen, Zhang Yinghao, Yu Lequan, Wang Liansheng, Yu Yizhou (2021) nnformer: Interleaved transformer for volumetric segmentation. CoRR, abs/2109.03201

  53. Hanna Siebert, Lasse Hansen, Heinrich Mattias P (2022) Fast 3d registration with accurate optimisation and little learning for learn2reg 2021. In: Aubreville Marc, Zimmerer David, Heinrich Mattias (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. Cham. Springer International Publishing, pp 174–179

    Google Scholar 

  54. Mok Tony CW, Chung Albert CS (2020) Large deformation diffeomorphic image registration with laplacian pyramid networks. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, and Leo Joskowicz (eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 211–221, Cham. Springer International Publishing

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant NO. 2020YFB1313900, the Shenzhen Science and Technology Program under Grant NO. JCYJ20200109115201707 and NO. JCYJ20220818101408019, and the Graduate Innovative Fund of Wuhan Institute of Technology under Grant NO. CX2023289.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, 430073, China

    Yilin Chen, Xin Hu, Tao Lu & Lu Zou

  2. Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430073, China

    Yilin Chen, Tao Lu & Lu Zou

  3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518052, China

    Xiangyun Liao

Authors
  1. Yilin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lu Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangyun Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyun Liao.

Ethics declarations

Conflict of interest

The authors state that they have no financial Conflict of interest or personal relationships that could have impacted the research presented in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Hu, X., Lu, T. et al. A multi-scale large kernel attention with U-Net for medical image registration. J Supercomput 81, 70 (2025). https://doi.org/10.1007/s11227-024-06489-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06489-9

Keywords

Search

Navigation