Asman Andrew J, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Inf Process Med Imaging. 2011;22:85-96. doi: 10.1007/978-3-642-22092-0_8.
Segmentation of medical images has become critical to building understanding of biological structure-functional relationships. Atlas registration and label transfer provide a fully-automated approach for deriving segmentations given atlas training data. When multiple atlases are used, statistical label fusion techniques have been shown to dramatically improve segmentation accuracy. However, these techniques have had limited success with complex structures and atlases with varying similarity to the target data. Previous approaches have parameterized raters by a single confusion matrix, so that spatially varying performance for a single rater is neglected. Herein, we reformulate the statistical fusion model to describe raters by regional confusion matrices so that co-registered atlas labels can be fused in an optimal, spatially varying manner, which leads to an improved label fusion estimation with heterogeneous atlases. The advantages of this approach are characterized in a simulation and an empirical whole-brain labeling task.
医学图像分割对于理解生物结构-功能关系至关重要。图谱配准和标签转移提供了一种在给定图谱训练数据的情况下推导分割结果的全自动方法。当使用多个图谱时，统计标签融合技术已被证明能显著提高分割精度。然而，这些技术在处理复杂结构以及与目标数据相似度各异的图谱时，取得的成功有限。以往的方法通过单个混淆矩阵对评分者进行参数化，从而忽略了单个评分者在空间上变化的表现。在此，我们重新构建统计融合模型，通过区域混淆矩阵来描述评分者，以便能以最优的、空间变化的方式融合共同配准的图谱标签，这会带来使用异质图谱时改进的标签融合估计。该方法的优势在一个模拟实验和一个全脑标记实证任务中得以体现。
