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Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Read chapter Read first chapter

2017 | OriginalPaper | Chapter

From Label Maps to Generative Shape Models: A Variational Bayesian Learning Approach

Authors : Shireen Y. Elhabian, Ross T. Whitaker

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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Abstract

This paper proposes a Bayesian treatment of a latent variable model for learning generative shape models of grid-structured representations, aka label maps, that relies on direct probabilistic formulation with a variational approach for deterministic model learning. Spatial coherency and sparsity priors are incorporated to lend stability to the optimization problem, thereby regularizing the solution space while avoiding overfitting in this high-dimensional, low-sample-size scenario. Hyperparameters are estimated in closed-form using type-II maximum likelihood to avoid grid searches. Further, a mixture formulation is proposed to capture nonlinear shape variations in a way that balances the model expressiveness with the efficiency of learning and inference. Experiments show that the proposed model outperforms state-of-the-art representations on real datasets w.r.t. generalization to unseen samples.

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previous chapter Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration
next chapter Constructing Shape Spaces from a Topological Perspective
Footnotes
1
Downloaded from the DIADIST project page: rbg-web2.rbge.org.uk/DIADIST/.
 
2
Downloaded from the national alliance for medical image computing (NAMIC) project page: https://​www.​na-mic.​org/​Wiki/​index.​php/​DBP3:​Utah.
 
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Metadata
Title
From Label Maps to Generative Shape Models: A Variational Bayesian Learning Approach
Authors
Shireen Y. Elhabian
Ross T. Whitaker
Copyright Year
2017
Book
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_8

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