Wang Hongzhi, Suh Jung Wook, Pluta John, Altinay Murat, Yushkevich Paul
PICSL, Department of Radiology, University of Pennsylvania, USA.
Inf Process Med Imaging. 2011;22:73-84. doi: 10.1007/978-3-642-22092-0_7.
Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.
基于多图谱的分割已在医学图像分析中得到广泛应用。对于标签融合，先前的研究表明，基于图像相似性的局部加权技术能产生最准确的结果。然而，这些方法忽略了不同图谱产生的结果之间的相关性。此外，它们依赖于预先选择的加权模型和临时方法来选择模型参数。我们提出了一种新颖的标签融合方法来解决这些局限性。我们的公式直接旨在降低组合误差的期望值，并且可以以封闭形式有效地求解。在我们的海马体分割实验中，我们的方法显著优于基于相似性的局部加权。使用20个图谱，我们得到的结果与对照组手动标注的骰子重叠率为0.898±0.019。
