Asman Andrew J, Dagley Alexander S, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90341E. doi: 10.1117/12.2043182.
Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally - fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy.
标签融合是许多图像分割框架（如多图谱分割）中的关键步骤，因为它提供了一种机制，可将一组带标签的示例归纳为对基础分割的单一估计。在多标签情况下，典型的标签融合算法对所有标签一视同仁，完全忽略了数据中呈现的已知但复杂的解剖关系。为了解决这个问题，我们提出了一种使用评分者性能层次模型的广义统计融合框架。基于统计融合方面的开创性工作，我们从多层层次视角重新构建了传统的评分者性能模型。这种新方法提供了一个自然的框架，用于利用已知的解剖关系，并在层次一致的公式中准确地对评分者（或图谱）所犯错误的类型进行建模。在此，我们描述了几个贡献。首先，我们对统计融合框架进行了理论推进，使得能够在统计融合背景下同时估计多个（层次）性能模型。其次，我们证明所提出的层次公式非常适合统计融合框架的最新进展。最后，在一项全脑分割实证任务中，我们展示了总体分割精度在定性和定量方面都有显著提高。
