Bayesian Estimation of Deformation and Elastic Parameters in Non-rigid Registration

Risholm, Petter; Samset, Eigil; Wells, William M.

doi:10.1007/978-3-642-14366-3_10

Cited by 23 publications

(27 citation statements)

References 10 publications

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“…However, recent registration methods have emerged that provide estimates of the registration uncertainty [7] [8]. This facilitates the consideration of a distribution of probable registration mappings, as opposed to just the MAP.…”

Section: Introductionmentioning

confidence: 99%

“…There are two published 3D medical image registration methods that we are aware of which infer a distribution of probable mappings: Risholm et al [7] use Markov chain Monte Carlo (MCMC) to numerically estimate the full posterior distribution of transformation parameters, whilst marginalising over the regularisation parameters. This method allows the estimation of a non-parametric posterior distribution of mappings.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning Incorporating Uncertain Registration

Simpson

Woolrich

Andersson

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalised analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalised feature data, e.g. grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel based morphometry and deformation tensor based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Incorporating Uncertain Registration

Simpson

Woolrich

Andersson

et al. 2013

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…A phantom study applied the method to the prostate [18], but the model and boundary conditions are greatly simplified, and their method has not been applied to real patient data. A Bayesian framework has also been proposed to solve the elastography problem without requiring known boundary conditions [20]. That method, however, depends on assumptions about probability distribution functions and extensive sampling in a very high dimensional parameter space (elasticity and boundary conditions), which significantly limits the number of boundary nodes.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Lee

Foskey

Niethammer

et al. 2012

IEEE Trans. Med. Imaging

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Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

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“…Tests are carried out for each simulated atrophy while combining results for all subjects (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) in the learning database, following a leave-one-out approach. Karacali et al's [8] method whose implementation is freely available on the internet.…”

Section: Tablementioning

confidence: 99%

“…Existing approaches of uncertainty estimation can be classified depending on whether they employ a ground truth [9,11], manipulate the similarity criterion chosen during the registration step [12] or the deformation field [13,14] or are based on Bayesian formulations [15][16][17]. All these existing methods, though developed and tested for other applications, can be employed or extended for brain atrophy estimations.…”

Section: Introductionmentioning

confidence: 98%