Zikic Darko, Glocker Ben, Criminisi Antonio
Microsoft Research Cambridge.
Med Image Comput Comput Assist Interv. 2013;16(Pt 3):66-73. doi: 10.1007/978-3-642-40760-4_9.
We propose a method for multi-atlas label propagation based on encoding the individual atlases by randomized classification forests. Most current approaches perform a non-linear registration between all atlases and the target image, followed by a sophisticated fusion scheme. While these approaches can achieve high accuracy, in general they do so at high computational cost. This negatively affects the scalability to large databases and experimentation. To tackle this issue, we propose to use a small and deep classification forest to encode each atlas individually in reference to an aligned probabilistic atlas, resulting in an Atlas Forest (AF). At test time, each AF yields a probabilistic label estimate, and fusion is done by averaging. Our scheme performs only one registration per target image, achieves good results with a simple fusion scheme, and allows for efficient experimentation. In contrast to standard forest schemes, incorporation of new scans is possible without retraining, and target-specific selection of atlases remains possible. The evaluation on three different databases shows accuracy at the level of the state of the art, at a significantly lower runtime.
我们提出了一种基于随机分类森林对个体图谱进行编码的多图谱标签传播方法。当前大多数方法在所有图谱与目标图像之间进行非线性配准，随后采用复杂的融合方案。虽然这些方法能够实现高精度，但总体而言它们是以高计算成本做到这一点的。这对扩展到大型数据库和进行实验产生了负面影响。为了解决这个问题，我们建议使用一个小型深度分类森林，参照对齐的概率图谱对每个图谱进行单独编码，从而得到一个图谱森林（AF）。在测试时，每个AF产生一个概率标签估计，并且通过平均进行融合。我们的方案对每个目标图像仅执行一次配准，通过简单的融合方案就能取得良好效果，并且允许进行高效的实验。与标准森林方案相比，无需重新训练就可以纳入新的扫描数据，并且仍然可以针对目标进行图谱的特定选择。在三个不同数据库上的评估表明，该方法在运行时间显著更低的情况下，达到了当前技术水平的精度。
