A generative probability model of joint label fusion for multi-atlas based brain segmentation
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Summary
A generative probability model is proposed to describe the procedure of label fusion in a multi-atlas scenario, with the goal of labeling each point in the target image by the best representative atlas patches that also have the largest labeling unanimity in labeling the underlying point correctly.
- Type
- article
- Published
- 2013-11-16
- Cited by
- 125
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W1969257438
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17114916
Keywords
Atlas (anatomy), Computer science, Artificial intelligence, Segmentation, Pattern recognition (psychology)
References
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- Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
- New methods of MR image intensity standardization via generalized scale
- Regression-Based Label Fusion for Multi-Atlas Segmentation
- Advances in functional and structural MR image analysis and implementation as FSL.
- Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
- Deformable segmentation via sparse representation and dictionary learning
- Coordinate descent algorithms for lasso penalized regression
- Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer’s disease
- Improving HAMMER Registration Algorithm by Soft Correspondence Matching and Thin-Plate Splines based Deformation Interpolation
- Sparse coding and decorrelation in primary visual cortex during natural vision.
- Structural MRI Biomarkers for Preclinical and Mild Alzheimer's Disease
- Feature‐based groupwise registration by hierarchical anatomical correspondence detection
- Structural maturation of neural pathways in children and adolescents: in vivo study.
- Diffeomorphic demons: Efficient non-parametric image registration
Cited by
- Segmenting Hippocampal Subfields from 3T MRI with Multi-modality Images
- Automatic brain labeling via multi‐atlas guided fully convolutional networks☆
- Towards Automatic Plan Selection for Radiotherapy of Cervical Cancer by Fast Automatic Segmentation of Cone Beam CT Scans
- Optimized PatchMatch for Near Real Time and Accurate Label Fusion
- Tissue-specific sparse deconvolution for brain CT perfusion
- An Optimized PatchMatch for multi-scale and multi-feature label fusion
- Brain Extraction in Pediatric ADC Maps, toward Characterizing Neuro-Development in Multi-Platform and Multi-Institution Clinical Images
- Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age
- Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change
- Multi-Atlas Segmentation of Biomedical Images: A Survey
- Multi-atlas based Segmentation Editing with Interaction-Guided Patch Selection and Label Fusion
- A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation
- Learning to Rank Atlases for Multiple-Atlas Segmentation
- Hierarchical Multi-atlas Label Fusion with Multi-scale Feature Representation and Label-specific Patch Partition
- Simultaneous and Consistent Labeling of Longitudinal Dynamic Developing Cortical Surfaces in Infants
- Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis
- Random local binary pattern based label learning for multi-atlas segmentation
- Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease
- Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials
- Patch-based Augmentation of Expectation-Maximization for Brain MRI Tissue Segmentation at Arbitrary Age after Premature Birth
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