Abstract
Accurate deformable image registration is important for brain analysis. However, there are two challenges in deformation registration of brain magnetic resonance (MR) images. First, the global cerebrospinal fluid (CSF) regions are rarely aligned since most of them are located in narrow regions outside of gray matter (GM) tissue. Second, the small complex morphological structures in tissues are rarely aligned since dense deformation fields are too blurred. In this work, we use a weakly supervised registration scheme, which is driven by global segmentation labels and local segmentation labels via two special loss functions. Specifically, multiscale double Dice similarity is used to maximize the overlap of the same labels and also minimize the overlap of regions with different labels. The structural similarity loss function is further used to enhance registration performance of small structures, thus enhancing the whole image registration accuracy. Experimental results on inter-subject registration of T1-weighted MR brain images from the OASIS-1 dataset show that the proposed scheme achieves higher accuracy on CSF, GM and white matter (WM) compared with the baseline learning model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Viergever, M.A., Maintz, J.B.A., Klein, S., Murphy, K., Staring, M., Pluim, J.P.W.: A survey of medical image registration – under review. Med. Image Anal. 33, 140–144 (2016)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31, 8 (2020). https://doi.org/10.1007/s00138-020-01060-x
Eppenhof, K.A.J., Lafarge, M.W., Pluim, J.P.W.: Progressively growing convolutional networks for end-to-end deformable image registration, vol. 48 (2019)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Dalca, A.V., Guttag, J.: An unsupervised learning model for deformable medical image registration. In: 2018 IEEE/CVF CVPR 2018, pp. 9252–9260. https://doi.org/10.1109/CVPR.2018.00964
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, 1788–1800 (2019). https://doi.org/10.1109/TMI.2019.2897538
Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformarle image registration. In: IEEE ISBI 2018, pp. 1070–1074 (2018)
Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1–13 (2018). https://doi.org/10.1016/j.media.2018.07.002
Mansilla, L., Milone, D.H., Ferrante, E.: Learning deformable registration of medical images with anatomical constraints. Neural Netw. 124, 269–279 (2020)
Li, B., et al.: A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 645–653. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_72
Lee, M.C.H., Oktay, O., Schuh, A., Schaap, M., Glocker, B.: Image-and-spatial transformer networks for structure-guided image registration. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 337–345. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_38
Xu, Z., Niethammer, M.: DeepAtlas: joint semi-supervised learning of image registration and segmentation (2019). http://arxiv.org/abs/1904.08465
Hoffmann, M., Billot, B., Iglesias, J.E., Fischl, B., Dalca, A.V.: Learning multi-modal image registration without real data. arXiv Prepr. 2004, pp. 1–12 (2020)
Estienne, T., et al.: U-ReSNet: ultimate coupling of registration and segmentation with deep nets. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 310–319. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_35
Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration – a deep learning approach. Neuroimage 158, 378–396 (2017)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019). https://doi.org/10.1016/j.media.2019.07.006
Heinrich, M.P.: Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 50–58. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_6
Ha, I.Y., Heinrich, M.P.: Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. In: Zhou, L., et al. (eds.) LABELS/HAL-MICCAI/CuRIOUS-2019. LNCS, vol. 11851, pp. 145–151. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33642-4_16
Sassi, O.B., Delleji, T., Taleb-Ahmed, A., Feki, I., Hamida, A.B.: MR image monomodal registration using structure similarity index. In: 2008 First Workshops on Image Processing Theory, Tools and Applications. pp. 1–5. IEEE (2008)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62, 782–790 (2012)
Luan, H., Qi, F., Xue, Z., Chen, L., Shen, D.: Multimodality image registration by maximization of quantitative–qualitative measure of mutual information. Pattern Recogn. 41(1), 285–298 (2008)
Wu, G., Qi, F., Shen, D.: Learning-based deformable registration of MR brain images. IEEE Trans. Med. Imaging 25(9), 1145–1157 (2006)
Acknowledgment
This work was supported in part by NSFC 61771230, 61773244, Shandong Provincial Natural Science Foundation ZR2019PF005, and Shandong Key R&D Program Project 2019GGX101006, 2019GNC106027. And we also thank for the open source code of Label-reg published by Hu Y et al.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, S., Zhang, L., Xu, Y., Shen, D. (2020). Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-59861-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59860-0
Online ISBN: 978-3-030-59861-7
eBook Packages: Computer ScienceComputer Science (R0)
