Zusammenfassung
Groupwise image registration and the estimation of anatomical shape variation play an important role for dealing with the analysis of large medical image datasets. In this work we adapt the concept of deforming autoencoders that decouples shape and appearance in an unsupervised learning setting, following a deformable template paradigm, and apply its capability for groupwise image alignment. We implement and evaluate this model for the application on medical image data and show its suitability for this domain by training it on middle slice MRI brain scans. Anatomical shape and appearance variation can be modeled by means of splitting a low-dimensional latent code into two parts that serve as inputs for separate appearance and shape decoder networks. We demonstrate the potential of deforming autoencoders to learn meaningful appearance and deformation representations of medical image data.
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Literatur
Guimond A, Meunier J, Thirion JP. Average brain models: A convergence study. Computer Vision and Image Understanding. 2000;77(2):192 – 210.
Joshi S, Davis B, Jomier M, et al. Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage. 2004;23:S151 – S160.
Che T, Zheng Y, Sui X, et al. DGR-Net: Deep groupwise registration of multispectral images. In: Chung ACS, editor. Information Processing in Medical Imaging. Cham: Springer International Publishing; 2019. p. 706–717.
Che T, Zheng Y, Cong J, et al. Deep group-wise registration for multi-spectral images from fundus images. IEEE Access. 2019;7:27650–27661.
Shu Z, Sahasrabudhe M, Güler RA, et al. Deforming Autoencoders: Unsupervised disentangling of shape and appearance. In: European Conference on Computer Vision; 2018. p. 664–680.
Krebs J, Delingette H, Mailhe B, et al. Learning a probabilistic model for diffeomorphic registration. IEEE Transactions on Medical Imaging. 2019;38(9):2165–2176.
Dalca A, Rakic M, Guttag J, et al. Learning conditional deformable templates with convolutional networks. In: Advances in neural information processing systems; 2019. p. 804–816.
Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks. In: Advances in neural information processing systems; 2015. p. 2017–2025.
Huang G, Liu Z, v d Maaten L, et al. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 2261–2269.
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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Siebert, H., Heinrich, M.P. (2020). Deep Groupwise Registration of MRI Using Deforming Autoencoders. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_53
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DOI: https://doi.org/10.1007/978-3-658-29267-6_53
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