Robust statistical label fusion through COnsensus Level, Labeler Accuracy, and Truth Estimation (COLLATE).
about
A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imagingMulti-atlas segmentation of biomedical images: A surveyAutomated methods for hippocampus segmentation: the evolution and a review of the state of the art.Groupwise multi-atlas segmentation of the spinal cord's internal structure.Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brainA logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weightsA Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest.Investigation of Bias in Continuous Medical Image Label Fusion.Optimal MAP Parameters Estimation in STAPLE Using Local Intensity Similarity InformationEstimating a reference standard segmentation with spatially varying performance parameters: local MAP STAPLEAutomated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar diseaseBrain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images.Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline.Self-assessed performance improves statistical fusion of image labels.Foibles, follies, and fusion: web-based collaboration for medical image labeling.Out-of-atlas likelihood estimation using multi-atlas segmentationEfficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.Non-local statistical label fusion for multi-atlas segmentation.Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.Formulating spatially varying performance in the statistical fusion framework.Performance of single and multi-atlas based automated landmarking methods compared to expert annotations in volumetric microCT datasets of mouse mandibles.Simultaneous truth and performance level estimation through fusion of probabilistic segmentations.Generalized Statistical Label Fusion using Multiple Consensus Levels.Collaborative Labeling of Malignant Glioma with WebMILL: A First LookLocal label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation.Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease.Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion.
P2860
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P2860
Robust statistical label fusion through COnsensus Level, Labeler Accuracy, and Truth Estimation (COLLATE).
description
2011 nî lūn-bûn
@nan
2011 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@ast
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@en
type
label
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@ast
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@en
prefLabel
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@ast
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@en
P2860
P356
P1476
Robust statistical label fusio ...... nd Truth Estimation (COLLATE).
@en
P2093
Andrew J Asman
Bennett A Landman
P2860
P304
P356
10.1109/TMI.2011.2147795
P577
2011-04-29T00:00:00Z