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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11070)

Abstract

Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu.

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Acknowledgments

This research was funded by NIH grants R01LM012719, R01AG053949, and 1R21AG050122, and NSF NeuroNex Grant grant 1707312.

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Correspondence to Adrian V. Dalca .

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Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R. (2018). Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_82

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  • DOI: https://doi.org/10.1007/978-3-030-00928-1_82

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