Abstract
Affine registration aims to find the low-dimensional parametric transformation that best aligns one data to another. However, existing registration methods, either classic energy optimization or deep learning are mainly designed for adult brain images and have limited performance on infant brain images with widely varied intensity distributions and low-contrast issues. To achieve fast and robust registration on low-contrast infant brain images, we propose an unsupervised deep registration framework DeepEnReg with a deep enhancement module and a deep affine registration module. Our affine registration module leverages a multi-resolution loss to guarantee consistency on sparsely sampled infant brain images. Our DeepEnReg achieves reasonable and reliable performance on the affine registration tasks of infant brain images and synthetic data and significantly reduces irregular registration results compared to other mainstream methods. Our proposed method significantly improves the computation efficiency over the mainstream medical image processing tools (from 13 to 0.570 s for a 3D image pair on affine registration) and outperforms state-of-the-art approaches.
Keywords
This work was partially supported by the National Key R &D Program of China (2020YFB1313503), the National Natural Science Foundation of China (Nos. 61922019), LiaoNing Revitalization Talents Program (XLYC1807088), and the Fundamental Research Funds for the Central Universities.
X. Wang—Student.
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Ahmad, S., et al.: Surface-constrained volumetric registration for the early developing brain. Med. Image Anal. 58, 101540 (2019)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imag. 38(8), 1788–1800 (2019)
Chen, X., Meng, Y., Zhao, Y., Williams, R., Vallabhaneni, S.R., Zheng, Y.: Learning unsupervised parameter-specific affine transformation for medical images registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 24–34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_3
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)
Dong, P., Wang, L., Lin, W., Shen, D., Wu, G.: Scalable joint segmentation and registration framework for infant brain images. Neurocomputing 229, 54–62 (2017)
Greve, D.N., Fischl, B.: Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48(1), 63–72 (2009)
Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780–1789 (2020)
Heinrich, M.P., et al.: Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423–1435 (2012)
Hu, S., Wei, L., Gao, Y., Guo, Y., Wu, G., Shen, D.: Learning-based deformable image registration for infant MR images in the first year of life. Med. Phys. 44(1), 158–170 (2017)
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782–790 (2012)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)
Li, Z., Li, Z., Liu, R., Luo, Z., Fan, X.: Automated learning for deformable medical image registration by jointly optimizing network architectures and objective functions. arXiv preprint arXiv:2203.06810 (2022)
Li, Z., Li, Z., Liu, R., Luo, Z., Fan, X.: Coupling deep deformable registration with contextual refinement for semi-supervised medical image segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)
Liu, R., Li, Z., Fan, X., Zhao, C., Huang, H., Luo, Z.: Learning deformable image registration from optimization: perspective, modules, bilevel training and beyond. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3115825
Liu, R., Li, Z., Zhang, Y., Fan, X., Luo, Z.: Bi-level probabilistic feature learning for deformable image registration. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 723–730 (2021)
Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10561–10570 (2021)
Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)
Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imag. 1(2), 1–6 (2014)
Sun, W., Niessen, W.J., Klein, S.: Free-form deformation using lower-order B-spline for nonrigid image registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 194–201. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10404-1_25
Wei, L., et al.: Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med. Phys. 44(12), 6289–6303 (2017)
Zhang, Y., Liu, R., Li, Z., Liu, Z., Fan, X., Luo, Z.: Coupling principled refinement with bi-directional deep estimation for robust deformable 3D medical image registration. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 86–90. IEEE (2020)
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)
Zhao, S., Lau, T., Luo, J., Eric, I., Chang, C., Xu, Y.: Unsupervised 3D end-to-end medical image registration with volume tweening network. IEEE J. Biomed. Health Inform. 24(5), 1394–1404 (2019)
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Wang, X. et al. (2022). DeepEnReg: Joint Enhancement and Affine Registration for Low-contrast Medical Images. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_13
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