Abstract
In this study, we propose a 3D deep neural network called U-ReSNet, a joint framework that can accurately register and segment medical volumes. The proposed network learns to automatically generate linear and elastic deformation models, trained by minimizing the mean square error and the local cross correlation similarity metrics. In parallel, a coupled architecture is integrated, seeking to provide segmentation maps for anatomies or tissue patterns using an additional decoder part trained with the dice coefficient metric. U-ReSNet is trained in an end to end fashion, while due to this joint optimization the generated network features are more informative leading to promising results compared to other deep learning-based methods existing in the literature. We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks. Our promising results indicate the potentials of our method which is composed from a convolutional architecture that is extremely simple and light in terms of parameters.
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
References
Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446–455 (2018)
Stergios, C., et al.: Linear and deformable image registration with 3D convolutional neural networks. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 13–22. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_2
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. LNCS, vol. 11070, pp. 729–738. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_82
Dong, P., Cao, X., Zhang, J., Kim, M., Wu, G., Shen, D.: Efficient groupwise registration for brain MRI by fast initialization. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 150–158. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_18
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). Advances in computing techniques for big medical image data
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Glocker, B., Komodakis, N., Navab, N., Tziritas, G., Paragios, N.: Dense registration with deformation priors. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 540–551. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02498-6_45
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
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)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_80
Postelnicu, G., Zollei, L., Fischl, B.: Combined volumetric and surface registration. IEEE Trans. Med. Imaging 28(4), 508–522 (2009)
Prevost, R., Cuingnet, R., Mory, B., Correas, J.-M., Cohen, L.D., Ardon, R.: Joint co-segmentation and registration of 3D ultrasound images. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 268–279. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_23
Robinson, E.C., et al.: Multimodal surface matching with higher-order smoothness constraints. NeuroImage 167, 453–465 (2018)
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
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)
Thewlis, J., Bilen, H., Vedaldi, A.: Modelling and unsupervised learning of symmetric deformable object categories. In: Advances in Neural Information Processing Systems 31. Curran Associates, Inc. (2018)
Vakalopoulou, M., et al.: AtlasNet: multi-atlas non-linear deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 658–666. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_75
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Estienne, T. et al. (2019). U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-32248-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32247-2
Online ISBN: 978-3-030-32248-9
eBook Packages: Computer ScienceComputer Science (R0)
