Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 31, Issue 6, Pages 1326-1336Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2012.2190992
Keywords
Multi-atlas segmentation; rater models; simultaneous truth and performance level estimation (STAPLE); spatial STAPLE; statistical fusion
Categories
Funding
- NIBIB NIH HHS [R01 EB006136, R03 EB012461, R01 EB006193] Funding Source: Medline
- NINDS NIH HHS [R21 NS064534] Funding Source: Medline
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To date, label fusion methods have primarily relied either on global [e. g., simultaneous truth and performance level estimation (STAPLE), globally weighted vote] or voxelwise (e. g., locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets.
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