Summarizing and Visualizing Uncertainty in Non-rigid Registration

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

doi:10.1007/978-3-642-15745-5_68

Cited by 51 publications

(57 citation statements)

References 9 publications

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“…Some previous work has been performed on visualising transformation uncertainty in non-rigid registration [11] [12]. More related work utilising the concept of uncertain registration includes: estimating local anisotropic smoothing kernels to compensate for uncertainty in registration when estimating spatially normalised statistics [13].…”

Section: Introductionmentioning

confidence: 99%

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%

Ensemble Learning Incorporating Uncertain Registration

Simpson

Woolrich

Andersson

et al. 2013

IEEE Trans. Med. Imaging

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show abstract

“…Since the hybrid registration combines information from two sources which they are intensity value and segmentation result information, this question can be further divided into uncertainty arising from the segmentation result [7,24] and uncertainty arising from abnormal intensity, which is related to functional abnormality of the subject. The registration uncertainty which comes from intensity relates to abnormal intensity like ischemic cardiomyopathy, while registration uncertainty which comes from segmented region (geometry distribution) corresponds well to abnormal geometry like dilated cardiomyopathy.…”

Section: Uncertainty Definition and Evaluationmentioning

confidence: 99%

“…Using a probabilistic formulation for the image registration problem [7], the uncertainty of a transformation at point can be modeled by the following equation:…”

Section: Uncertainty Definitionmentioning

confidence: 99%

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Uncertainty Estimation for Improving Accuracy of Non-rigid Registration in Cardiac Images

Mazaheri

Sulaiman

Rahmat

et al. 2015

IFIP Advances in Information and Communication Technology

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Part 1: Image Processing and AnalysisInternational audienceIn order to utilize both computed tomography (CT) and echocardiography images of the heart for medical applications such as diagnosis and image guided intervention concurrently, non-rigid registration is an essential task. A challenging but important problem in image registration is evaluating the performance of a registration algorithm. The direct quantitative approach is to compare the deformation field solution with the ground truth transformation (at all or some landmark pixels). However, in clinical data, the ground truth is typically unknown. To deal with the absence of ground truth, some methods opted to estimate registration accuracy by using uncertainty measures as a surrogate for quantitative registration error. In this paper, we define the registration uncertainty and explore its use for diagnostic purposes. We use uncertainty estimation for improving accuracy of a hybrid registration which register a pre-operative CT to an intra-operative echocardiography images. In other words, uncertainty estimation is used to evaluate the registration algorithm performance which integrates intensity-based and feature-based methods. This registration can potentially be used to improve the diagnosis of cardiac disease by augmenting echocardiography images with high-resolution CT images and to facilitate intra-operative image fusion for minimally invasive cardio-thoracic surgical navigation. Here, we show how to determine the registration uncertainty, by using uncertainty quantification regarding to abnormal intensity and geometry distribution. The result indicates that registration uncertainty is a good predictor for the functional abnormality of subjects

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“…Registration uncertainty can be utilized to direct clinicians to possible registration errors or image abnormalities, and can influence diagnostic and therapeutic decisions. [9,4,7].…”

Section: Introductionmentioning

confidence: 99%

Topology Preservation and Anatomical Feasibility in Random Walker Image Registration

Andrews

Tang

Hamarneh

2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. The random walker image registration (RWIR) method is a powerful tool for aligning medical images that also provides useful uncertainty information. However, it is difficult to ensure topology preservation in RWIR, which is an important property in medical image registration as it is often necessary for the anatomical feasibility of an alignment. In this paper, we introduce a technique for determining spatially adaptive regularization weights for RWIR that ensure an anatomically feasible transformation. This technique only increases the run time of the RWIR algorithm by about 10%, and avoids over-smoothing by only increasing regularization in specific image regions. Our results show that our technique ensures topology preservation and improves registration accuracy.

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Summarizing and Visualizing Uncertainty in Non-rigid Registration

Cited by 51 publications

References 9 publications

Ensemble Learning Incorporating Uncertain Registration

Ensemble Learning Incorporating Uncertain Registration

Uncertainty Estimation for Improving Accuracy of Non-rigid Registration in Cardiac Images

Topology Preservation and Anatomical Feasibility in Random Walker Image Registration

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