Abstract
Image registration is an essential task in electron microscope (EM) image analysis, which aims to accurately warp the moving image to align with the fixed image, to reduce the spatial deformations across serial slices resulted during image acquisition. Existing learning-based registration approaches are primarily based on Convolution Neural Networks (CNNs). However, for the requirements of EM image registration, CNN-based methods lack the capability of learning global and long-term semantic information. In this work, we propose a new framework, Cascaded LST-UNet, which integrates a sharpening skip-connection layer with the Swin Transformer based U-Net structure in a cascaded manner for unsupervised EM image registration. Our experimental results on a public dataset show that our method consistently outperforms the baseline approaches.
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References
Arganda-Carreras, I., Sorzano, C.O., Kybic, J., Ortiz-de Solorzano, C.: bUnwarpJ: consistent and elastic registration in ImageJ, methods and applications. In: Second ImageJ User and Developer Conference, vol. 12 (2008)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medial image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Trans. Image Process. 21(4), 1488–1499 (2011)
Cao, H., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. arXiv:2105.05537 (2021)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part I. LNCS, vol. 11070, pp. 729–738. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_82
Fu, D., Kuduvalli, G.: A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery. Med. Phys. 35(5), 2180–2194 (2008)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial Transformer Networks. In: Neural Information Processing Systems (2015)
Kim, B., Kim, D.H., Park, S.H., Kim, J., Lee, J.G., Ye, J.: CycleMorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (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. Imaging 29(1), 196–205 (2009)
Liu, Z., et al.: Swin Transformer: Hierarchical vision transformer using shifted windows. arXiv preprint arXiv:2103.14030 (2021)
Lv, J., Yang, M., Zhang, J., Wang, X.: Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study. Brit. J. Radiol. 91, 20170788 (2018)
Mok, T.C., Chung, A.: Fast symmetric diffeomorphic image registration with convolutional neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4644–4653 (2020)
Nguyen-Duc, T., Yoo, I., Thomas, L., Kuan, A., Lee, W., Jeong, W.: Weakly supervised learning in deformable EM image registration using slice interpolation. In: International Symposium on Biomedical Imaging (ISBI), pp. 670–673 (2019)
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, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sedgwick, P.: Pearson’s correlation coefficient. BMJ 345, e4483–e4483 (2012)
de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 204–212. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_24
Xin, T., Chen, B., Chen, X., Han, H.: UTR: unsupervised learning of thickness-insensitive representations for electron microscope image. In: IEEE International Conference on Image Processing (ICIP), pp. 155–159 (2021)
Yoo, I., Hildebrand, D.G.C., Tobin, W.F., Lee, W.-C.A., Jeong, W.-K.: ssEMnet: serial-section electron microscopy image registration using a spatial transformer network with learned features. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 249–257. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_29
Zhang, L., Zhou, L., Li, R., Wang, X., Han, B., Liao, H.: Cascaded feature warping network for unsupervised medical image registration. In: International Symposium on Biomedical Imaging (ISBI), pp. 913–916 (2021)
Zhao, S., Dong, Y., Chang, E.I., Xu, Y., et al.: Recursive cascaded networks for unsupervised medical image registration. In: International Conference on Computer Vision (ICCV), pp. 10600–10610 (2019)
Zhou, S., et al.: Fast and accurate electron microscopy image registration with 3D convolution. In: Shen, D., et al. (eds.) MICCAI 2019, Part I. LNCS, vol. 11764, pp. 478–486. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_53
Zunair, H., Hamza, A.B.: Sharp U-Net: Depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med. 136, 104699 (2021)
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Xie, K., Yang, Y., Pagnucco, M., Song, Y. (2022). Electron Microscope Image Registration Using Laplacian Sharpening Transformer U-Net. 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_30
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