Xu Zhoubing, Asman Andrew J, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Proc SPIE Int Soc Opt Eng. 2012 Feb 23;8314. doi: 10.1117/12.910918.
Segmentation plays a critical role in exposing connections between biological structure and function. The process of label fusion collects and combines multiple observations into a single estimate. Statistically driven techniques provide mechanisms to optimally combine segmentations; yet, optimality hinges upon accurate modeling of rater behavior. Traditional approaches, e.g., Majority Vote and Simultaneous Truth and Performance Level Estimation (STAPLE), have been shown to yield excellent performance in some cases, but do not account for spatial dependences of rater performance (i.e., regional task difficulty). Recently, the COnsensus Level, Labeler Accuracy and Truth Estimation (COLLATE) label fusion technique augmented the seminal STAPLE approach to simultaneously estimate regions of relative consensus versus confusion along with rater performance. Herein, we extend the COLLATE framework to account for multiple consensus levels. Toward this end, we posit a generalized model of rater behavior of which Majority Vote, STAPLE, STAPLE Ignoring Consensus Voxels, and COLLATE are special cases. The new algorithm is evaluated with simulations and shown to yield improved performance in cases with complex region difficulties. Multi-COLLATE achieve these results by capturing different consensus levels. The potential impacts and applications of generative model to label fusion problems are discussed.
分割在揭示生物结构与功能之间的联系方面起着关键作用。标签融合过程将多个观察结果收集并合并为一个单一估计值。统计驱动技术提供了优化合并分割的机制；然而，最优性取决于对评分者行为的准确建模。传统方法，例如多数投票法和同时估计真值与性能水平法（STAPLE），在某些情况下已被证明具有出色的性能，但没有考虑评分者性能的空间依赖性（即区域任务难度）。最近，共识水平、标注者准确性和真值估计（COLLATE）标签融合技术扩展了开创性的STAPLE方法，以同时估计相对共识区域与混淆区域以及评分者性能。在此，我们扩展COLLATE框架以考虑多个共识水平。为此，我们提出了一个评分者行为的广义模型，多数投票法、STAPLE法、忽略共识体素的STAPLE法和COLLATE法都是该广义模型的特殊情况。通过模拟对新算法进行了评估，结果表明在区域难度复杂的情况下该算法性能有所提高。多COLLATE通过捕捉不同的共识水平实现了这些结果。讨论了生成模型对标签融合问题的潜在影响和应用。
