Formulating spatially varying performance in the statistical fusion framework.
Formulating spatially varying performance in the statistical fusion framework.
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DOI：
10.1109/tmi.2012.2190992
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发表时间：
2012-06
影响因子：
10.6
通讯作者：
Landman BA
中科院分区：
文献类型：
--
作者：
Asman AJ;Landman BA
迄今为止，标签融合方法主要依赖于全局（例如STAPLE，全局加权投票）或体素（例如局部加权投票）性能模型。统计融合框架的最优性取决于评定者如何犯错的随机模型的有效性（即，标记过程模型）。目前，各种方法往往侧重于潜在模型的极端情况。在这里，我们提出了一个扩展的STAPLE方法，无缝地考虑到空间变化的性能，通过扩展性能水平参数，以考虑到一个平滑的，逐体素的性能水平字段，这是唯一的每个评价者。这种方法，空间吻合器，提供了显着的改进，超过国家的最先进的标签融合算法在模拟和经验数据集。
To date, label fusion methods have primarily relied either on global (e.g. 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.