Longitudinal Brain MRI Analysis with Uncertain Registration
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Summary
A novel approach for incorporating measures of spatial uncertainty, which are derived from non-rigid registration, into spatially normalised statistics, from a probabilistic registration framework, which provides a principled approach to image smoothing.
- Type
- article
- Published
- 2011-09-18
- Cited by
- 36
- References
- 22
- Access
- Open access
- OpenAlex
- https://openalex.org/W1831299220
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15384359
Keywords
Smoothing, Computer science, Image registration, Artificial intelligence, Probabilistic logic
References
- An Introduction to Variational Methods for Graphical Models
- Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III
- Summarizing and Visualizing Uncertainty in Non-Rigid Registration
- Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis
- Alzheimer’s Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition
- Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images.
- Probabilistic inference of regularisation in non-rigid registration
- Towards a coherent statistical framework for dense deformable template estimation
- The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods
- Linked independent component analysis for multimodal data fusion
- Bayesian modeling of uncertainty in low-level vision
- A Stochastic Approach to Estimate the UncertaintyInvolved in B-Spline Image Registration
- Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
- Bootstrap Resampling for Image Registration Uncertainty Estimation Without Ground Truth
- Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data
- Mapping the evolution of regional atrophy in Alzheimer's disease: Unbiased analysis of fluid-registered serial MRI
- A fast diffeomorphic image registration algorithm
- Probabilistic Segmentation Propagation from Uncertainty in Registration
- アルツハイマー病の早期診断に向けて-米国 Alzheimer's Disease Neuroimaging Initiative の取り組み
- A Variational Baysian Framework for Graphical Models
Cited by
- Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach
- Fully Automated Medical Image Analysis Facilitating Subsequent User Analysis
- Uncertainty in probabilistic image registration
- Towards Adaptive Radiotherapy through Development of Treatment Response Prediction
- Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfield Volumetry
- Incremental projection approach of regularization for inverse problems
- Ensemble Learning Incorporating Uncertain Registration
- Robust non-rigid registration and characterization of uncertainty
- Deformable Templates Guided Discriminative Models for Robust 3D Brain MRI Segmentation
- An augmented parametric response map with consideration of image registration error: towards guidance of locally adaptive radiotherapy
- Probabilistic inference of regularisation in non-rigid registration
- An approach to identify, from DCE MRI, significant subvolumes of tumors related to outcomes in advanced head-and-neck cancer.
- Bayesian Characterization of Uncertainty in Intra-Subject Non-Rigid Registration
- Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry
- Atlas Construction for Measuring the Variability of Complex Anatomical Structures
- Probabilistic Segmentation Propagation from Uncertainty in Registration
- Assessment of Rigid Registration Quality Measures in Ultrasound-Guided Radiotherapy
- Imaging biomarkers in paediatric brain resection MRI.
- Towards automated dynamic scene analysis and augmentation during image-guided radiological and surgical interventions
- Deformation Estimation and Assessment of Its Accuracy in Ultrasound Images
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