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2018 | OriginalPaper | Chapter

Probabilistic Fitting of Active Shape Models

Authors : Andreas Morel-Forster, Thomas Gerig, Marcel Lüthi, Thomas Vetter

Published in: Shape in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Active Shape Models (ASMs) are a classical and widely used approach for fitting shape models to images. In this paper, we propose a fully probabilistic interpretation of ASM fitting as Bayesian inference. To infer the posterior, we use the Metropolis-Hastings algorithm. We then use the maximum a posteriori sample as the segmentation result. Our approach has several advantages compared to classical ASM fitting: (1) We are left with fewer parameters that we need to choose. (2) It is less prone to get trapped in local minima. (3) It becomes straightforward to extend the approach to include additional information, such as expert annotations. (4) It is even simpler to implement than the classical ASM fitting method.

We apply our algorithm to the SLIVER dataset and show that it achieves a higher segmentation accuracy than the standard ASM approach. We further demonstrate the flexibility and expressivity of the framework by integrating experts annotations along parts of the outline to further increase the accuracy. The code used for fitting is based on open-source software and made available to the community.

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previous chapter 4D Continuous Medial Representation Trajectory Estimation for Longitudinal Shape Analysis

next chapter Automatic Extraction of a Piecewise Symmetry Surface of a 3D Mesh: Application to Scoliosis

Note that, while the mathematically concepts are equivalent to the classical ASM papers [1, 2], our exposition of PDMs is based on the notation and interpretation of Point Distribution Models as Discrete Gaussian processes, as presented by Lüthi et al. [9].

The code for the model adaptation is available online at github.com/unibas-gravis/probabilistic-fitting-ASM.

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Title: Probabilistic Fitting of Active Shape Models
Authors: Andreas Morel-Forster
Thomas Gerig
Marcel Lüthi
Thomas Vetter
Publisher: Springer International Publishing
Book: Shape in Medical Imaging
Print ISBN: 978-3-030-04746-7

Electronic ISBN: 978-3-030-04747-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-04747-4_13

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