Abstract
An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration. However, the existing deep networks focus on single image situation and are limited in registration task which is performed on paired images. Therefore, we advance a novel backbone network, XMorpher, for the effective corresponding feature representation in DMIR. 1) It proposes a novel full transformer architecture including dual parallel feature extraction networks which exchange information through cross attention, thus discovering multi-level semantic correspondence while extracting respective features gradually for final effective registration. 2) It advances the Cross Attention Transformer (CAT) blocks to establish the attention mechanism between images which is able to find the correspondence automatically and prompts the features to fuse efficiently in the network. 3) It constrains the attention computation between base windows and searching windows with different sizes, and thus focuses on the local transformation of deformable registration and enhances the computing efficiency at the same time. Without any bells and whistles, our XMorpher gives Voxelmorph 2.8% improvement on DSC, demonstrating its effective representation of the features from the paired images in DMIR. We believe that our XMorpher has great application potential in more paired medical images. Our XMorpher is open on https://github.com/Solemoon/XMorpher
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)
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)
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Chen, J., Du, Y., He, Y., Segars, W.P., Li, Y., Frey, E.C.: Transmorph: transformer for unsupervised medical image registration. arXiv preprint arXiv:2111.10480 (2021)
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. Med. Image Anal. 52, 128–143 (2019)
Gharleghi, R., Samarasinghe, D.G., Sowmya, P.A., Beier, D.S.: Automated segmentation of coronary arteries, March 2020. https://doi.org/10.5281/zenodo.3819799, https://doi.org/10.5281/zenodo.3819799
Han, K., et al.: A survey on vision transformer. IEEE In: Transactions on Pattern Analysis and Machine Intelligence (2022)
He, Y., et al.: Few-shot learning for deformable medical image registration with perception-correspondence decoupling and reverse teaching. IEEE J. Biomed. Health Inform. 26, 1177–1187 (2021)
He, Y., Li, T., Yang, G., Kong, Y., Chen, Y., Shu, H., Coatrieux, J.-L., Dillenseger, J.-L., Li, S.: Deep complementary joint model for complex scene registration and few-shot segmentation on medical images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 770–786. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58523-5_45
Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (CSUR) (2021)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imag. 29(1), 196–205 (2009)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: A survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Tustison, N., et al.: Large-scale evaluation of ants and freesurfer cortical thickness measurements. Neuroimage 99, 166–179 (2014)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vos, B.D.d., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: 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, pp. 204–212. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9
Zhang, Y., Pei, Y., Zha, H.: Learning dual transformer network for diffeomorphic registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 129–138. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_13
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation under grants (62171125, 61828101), CAAI-Huawei MindSpore Open Fund, CANN (Compute Architecture for Neural Networks), Ascend AI Processor, and Big Data Computing Center of Southeast University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, J. et al. (2022). XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-16446-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16445-3
Online ISBN: 978-3-031-16446-0
eBook Packages: Computer ScienceComputer Science (R0)