A Static SMC Sampler on Shapes for the Automated Segmentation of Aortic Calcifications

Petersen, Kathrine Birch; Nielsen, Mads; Brandt, Sami S.

doi:10.1007/978-3-642-15561-1_48

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…In prior work, Monte-Carlo approaches have been used to characterize posterior distributions on segmentations, which require sampling [24,18,45]. In addition, the Monte-Carlo approach is quite general about statistical modeling assumptions so that it could be applied to the likelihood and regularity terms of our segmentation tasks.…”

Section: Contributions the Main Contributions Of This Article Arementioning

confidence: 99%

Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan--Vese and Rudin--Osher--Fatemi Models

Niethammer¹,

Pohl²,

Janoos³

et al. 2017

SIAM J. Imaging Sci.

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Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan–Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler–Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the dataset.

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Section: Contributions the Main Contributions Of This Article Arementioning

confidence: 99%

Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan--Vese and Rudin--Osher--Fatemi Models

Niethammer¹,

Pohl²,

Janoos³

et al. 2017

SIAM J. Imaging Sci.

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“…Other methods (De Bruijne and Nielsen (2004); Petersen et al (2010)) rely on sampling in order to find the most probable segmentation. It is particularly useful when the object of interest is not clearly visible or partially occluded.…”

Section: Introductionmentioning

confidence: 99%

Sampling image segmentations for uncertainty quantification

Unkelbach

Ayache

et al. 2016

Medical Image Analysis

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In this paper, we introduce a method to automatically produce plausible image segmentation samples from a single expert segmentation. A probability distribution of image segmentation boundaries is defined as a Gaussian process, which leads to segmentations which are spatially coherent and consistent with the presence of salient borders in the image. The proposed approach is computationally efficient, and generates visually plausible samples. The variability between the samples is mainly governed by a parameter which may be correlated with a simple Dice's coefficient, or easily set by the user from the definition of probable regions of interest. The method is extended to the case of several neighboring structures, but also to account for under or over segmentation, and the presence of excluded regions. We also detail a method to sample segmentations with more general non-stationary covariance functions which relies on supervoxels. Furthermore, we compare the generated segmentation samples with several manual clinical segmentations of a brain tumor. Finally, we show how this approach can have useful applications in the field of uncertainty quantification, and an illustration is provided in radiotherapy planning, where segmentation sampling is applied to both the clinical target volume and the organs at risk.

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