Xing Fangxu, Asman Andrew J, Prince Jerry L, Landman Bennett A
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA 21218.
Proc SPIE Int Soc Opt Eng. 2012 Feb 23;8314. doi: 10.1117/12.911524.
Image labeling is an essential step for quantitative analysis of medical images. Many image labeling algorithms require seed identification in order to initialize segmentation algorithms such as region growing, graph cuts, and the random walker. Seeds are usually placed manually by human raters, which makes these algorithms semi-automatic and can be prohibitive for very large datasets. In this paper an automatic algorithm for placing seeds using multi-atlas registration and statistical fusion is proposed. Atlases containing the centers of mass of a collection of neuroanatomical objects are deformably registered in a training set to determine where these centers of mass go after labels transformed by registration. The biases of these transformations are determined and incorporated in a continuous form of Simultaneous Truth And Performance Level Estimation (STAPLE) fusion, thereby improving the estimates (on average) over a single registration strategy that does not incorporate bias or fusion. We evaluate this technique using real 3D brain MR image atlases and demonstrate its efficacy on correcting the data bias and reducing the fusion error.
图像标注是医学图像定量分析的关键步骤。许多图像标注算法需要进行种子点识别，以便初始化诸如区域生长、图割和随机游走等分割算法。种子点通常由人工评分者手动放置，这使得这些算法具有半自动性质，并且对于非常大的数据集来说可能成本过高。本文提出了一种使用多图谱配准和统计融合来放置种子点的自动算法。包含一组神经解剖对象质心的图谱在训练集中进行可变形配准，以确定在通过配准变换标签后这些质心的位置。确定这些变换的偏差并将其纳入同时真值与性能水平估计（STAPLE）融合的连续形式中，从而相对于不纳入偏差或融合的单一配准策略（平均而言）改进估计。我们使用真实的三维脑磁共振图像图谱评估了该技术，并证明了其在纠正数据偏差和减少融合误差方面的有效性。
