Abstract
Groupwise registration of multispectral images (MSI) is clinically essential to facilitate accurate information fusion across different modalities. However, the groupwise registration of multispectral images is a challenging task because multiple different imaging modalities makes it difficult to jointly optimize the deformation. In this work, we propose an unbiased deep groupwise registration framework, DGR-Net, which takes a complete consideration of the information aggregated by calculating the deformation of the sequence image. Our framwork guided by principal component analysis (PCA) image. Network optimization is accelerated by combining internal smoothing and external correlation of the deformation fields. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging multi-modality groupwise registration task and also outperforms the state-of-the-art approaches.
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References
Althof, R.J., Wind, M.G., Dobbins, J.T.: A rapid and automatic image registration algorithm with subpixel accuracy. IEEE Trans. Med. Imaging 16(3), 308–316 (1997)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Bing, X., Wang, N., Chen, T., Mu, L.: Empirical evaluation of rectified activations in convolutional network. Computer Science (2015)
Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., Shen, D.: Deep learning based inter-modality image registration supervised by intra-modality similarity. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 55–63. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_7
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Goshtasby, A., Stockman, G.C.: Point pattern matching using convex hull edges. IEEE Trans. Syst. Man Cybern. (5), 631–637 (1985)
Hanaizumi, N., Fujimur, S.: An automated method for registration of satellite remote sensing images. In: International Geoscience and Remote Sensing Symposium, IGARSS 1993, Better Understanding of Earth Environment, vol. 3, pp. 1348–1350 (1993)
He, K., Zhang, X., Ren, S., Jian, S.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks, pp. 2017–2025 (2015)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)
Jia, H., Yap, P.T., Wu, G., Wang, Q., Shen, D.: Intermediate templates guided groupwise registration of diffusion tensor images. NeuroImage 54(2), 928–939 (2011)
Joshi, S., Davis, B., Jomier, M.G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23(1), S151–S160 (2004)
Ketkar, N.: Introduction to PyTorch. In: Ketkar, N. (ed.) Deep Learning with Python, pp. 195–208. Apress, Berkeley (2017). https://doi.org/10.1007/978-1-4842-2766-4_12
Liao, R., et al.: An artificial agent for robust image registration (2016)
Polfliet, M., Klein, S., Huizinga, W., Paulides, M.M., Niessen, W.J., Vandemeulebroucke, J.: Intrasubject multimodal groupwise registration with the conditional template entropy. Med. Image Anal. 46, 15–25 (2018)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on International Conference on Machine Learning (2010)
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
Rorden, C., Brett, M.: Stereotaxic display of brain lesions. Behav. Neurol. 12(4), 191–200 (2000)
Che, T., et al.: Deep group-wise registration for multi-spectral images from fundus images. In: IEEE Access (2019)
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
Wachinger, C., Wein, W., Navab, N.: Three-dimensional ultrasound mosaicing. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 327–335. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75759-7_40
Wulff, J., Black, M.J.: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: Computer Vision and Pattern Recognition, pp. 120–130 (2015)
Zhao, B., et al.: Joint alignment of multispectral images via semidefinite programming. Biomed. Opt. Express 8(2), 890 (2017)
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Che, T. et al. (2019). DGR-Net: Deep Groupwise Registration of Multispectral Images. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_55
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