Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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2016 nî lūn-bûn

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2016年の論文

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2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

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2016年论文

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2016年论文

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Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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Investigation of Bias in Continuous Medical Image Label Fusion.

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P1476

Investigation of Bias in Continuous Medical Image Label Fusion.

@en

P2093

Bennett A Landman

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Fangxu Xing

http://www.w3.org/2001/XMLSchema#string

Jerry L Prince

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P2860

A generative model for image segmentation based on label fusion

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Robust statistical label fusion through COnsensus Level, Labeler Accuracy, and Truth Estimation (COLLATE).

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

Validation of image segmentation by estimating rater bias and variance

An Empirical Study Into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation.

A continuous STAPLE for scalar, vector, and tensor images: an application to DTI analysis.

Robust statistical fusion of image labels.

Regression-Based Label Fusion for Multi-Atlas Segmentation.

P304

e0155862

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scholarly article

P356

10.1371/JOURNAL.PONE.0155862

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P407

English

P433

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P478

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P577

2016-06-03T00:00:00Z

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27258158

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P921

bias

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4892597

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