Abstract
Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain or heart which have limited or predictable range of deformations. Here we propose an encoding-decoding CNN architecture that can exploit rough atlases that encode only the topology of the target structures that can appear in any pose and have arbitrarily complex shapes to improve the segmentation results. It relies on the output of the encoder to compute both the pose parameters used to deform the atlas and the segmentation mask itself, which makes it effective and end-to-end trainable.
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
Atzeni, A., Jansen, M., Ourselin, S., Iglesias, J.E.: A probabilistic model combining deep learning and multi-atlas segmentation for semi-automated labelling of histology. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 219–227. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_25
Balakrishnan, G., Zhao, A., Dalca, A., Durand, F., Guttag, J.: Synthesizing images of humans in unseen poses. In: CVPR (2018)
Bertasius, G., Park, H.S., Yu, S.X., Shi, J.: Unsupervised learning of important objects from first-person videos. In: ICCV (2017)
Dong, S., et al.: VoxelAtlasGAN: 3D left ventricle segmentation on echocardiography with atlas guided generation and voxel-to-voxel discrimination. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 622–629. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_71
Fei, W., et al.: Residual attention network for image classification. In: CVPR (2017)
Hering, A., Kuckertz, S., Heldmann, S., Heinrich, M.P.: Enhancing label-driven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking. In: Bildverarbeitung für die Medizin (2019)
Huo, Y., et al.: Spatially localized atlas network tiles enables 3D whole brain segmentation from limited data. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 698–705. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_80
Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24, 205–219 (2015)
Januszewski, M., et al.: High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605 (2018)
Katouzian, A., et al.: Hashing-based atlas ranking and selection for multiple-atlas segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 543–551. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_62
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565–571, October 2016
Retinal fundus glaucoma challenge (2018). http://refuge.grand-challenge.org. Accessed 24 Jun 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. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Spitzer, H., Amunts, K., Harmeling, S., Dickscheid, T.: Parcellation of visual cortex on high-resolution histological brain sections using convolutional neural networks. In: ISBI, pp. 920–923 (2017)
Spitzer, H., Kiwitz, K., Amunts, K., Harmeling, S., Dickscheid, T.: Improving cytoarchitectonic segmentation of human brain areas with self-supervised siamese networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 663–671. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_76
Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.K.: Efficient video object segmentation via network modulation. In: CVPR (2018)
Acknowledgments
This work was supported in part by a Swiss National Science Foundation grant.
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
Wickramasinghe, U., Knott, G., Fua, P. (2019). Probabilistic Atlases to Enforce Topological Constraints. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)
