Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands.
IEEE Trans Med Imaging. 2010 Dec;29(12):2000-8. doi: 10.1109/TMI.2010.2057442. Epub 2010 Jul 26.
In a multi-atlas based segmentation procedure, propagated atlas segmentations must be combined in a label fusion process. Some current methods deal with this problem by using atlas selection to construct an atlas set either prior to or after registration. Other methods estimate the performance of propagated segmentations and use this performance as a weight in the label fusion process. This paper proposes a selective and iterative method for performance level estimation (SIMPLE), which combines both strategies in an iterative procedure. In subsequent iterations the method refines both the estimated performance and the set of selected atlases. For a dataset of 100 MR images of prostate cancer patients, we show that the results of SIMPLE are significantly better than those of several existing methods, including the STAPLE method and variants of weighted majority voting.
在基于多图谱的分割过程中，传播的图谱分割必须在标签融合过程中进行组合。一些当前的方法通过使用图谱选择来构建图谱集来解决这个问题，这些图谱集要么在注册之前构建，要么在注册之后构建。其他方法则估计传播分割的性能，并将该性能用作标签融合过程中的权重。本文提出了一种用于性能水平估计的选择性和迭代方法（SIMPLE），该方法将这两种策略结合在一个迭代过程中。在后续的迭代中，该方法会改进估计的性能和所选图谱集。对于一个包含 100 张前列腺癌患者的磁共振图像数据集，我们表明 SIMPLE 的结果明显优于包括 STAPLE 方法和加权多数投票变体在内的几种现有方法。
