Wu Guorong, Wang Qian, Zhang Daoqiang, Nie Feiping, Huang Heng, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Med Image Anal. 2014 Aug;18(6):881-90. doi: 10.1016/j.media.2013.10.013. Epub 2013 Nov 16.
Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling has been widely investigated to alleviate the possible mis-alignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these limitations, we propose a generative probability model to describe the procedure of label fusion in a multi-atlas scenario, for the goal of labeling each point in the target image by the best representative atlas patches that also have the largest labeling unanimity in labeling the underlying point correctly. Specifically, sparsity constraint is imposed upon label fusion weights, in order to select a small number of atlas patches that best represent the underlying target patch, thus reducing the risks of including the misleading atlas patches. The labeling unanimity among atlas patches is achieved by exploring their dependencies, where we model these dependencies as the joint probability of each pair of atlas patches in correctly predicting the labels, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. The patch dependencies will be further recursively updated based on the latest labeling results to correct the possible labeling errors, which falls to the Expectation Maximization (EM) framework. To demonstrate the labeling performance, we have comprehensively evaluated our patch-based labeling method on the whole brain parcellation and hippocampus segmentation. Promising labeling results have been achieved with comparison to the conventional patch-based labeling method, indicating the potential application of the proposed method in the future clinical studies.
在许多神经科学研究中，医学图像中解剖结构的自动标注非常重要。最近，基于补丁的标注方法得到了广泛研究，以减轻在将图谱注册到目标图像时可能出现的错位问题。然而，用于从注册图谱进行标签融合的权重通常是独立计算的，因此缺乏防止模糊的图谱补丁对标签融合产生影响的能力。更关键的是，这些权重通常仅基于简单的补丁相似度计算，因此不一定能为标签融合提供最优解。为了解决这些局限性，我们提出了一种生成概率模型来描述多图谱场景下的标签融合过程，目标是通过最佳代表性的图谱补丁对目标图像中的每个点进行标注，这些补丁在正确标注基础点时也具有最大的标注一致性。具体而言，对标签融合权重施加稀疏性约束，以选择少量最能代表基础目标补丁的图谱补丁，从而降低包含误导性图谱补丁的风险。通过探索图谱补丁之间的依赖性来实现图谱补丁之间的标注一致性，我们将这些依赖性建模为每对图谱补丁在正确预测标签时的联合概率，通过分析它们的形态学误差模式的相关性以及图谱之间的标注共识来实现。补丁依赖性将根据最新的标注结果进一步递归更新，以纠正可能的标注错误，这属于期望最大化（EM）框架。为了展示标注性能，我们在全脑分割和海马体分割上全面评估了我们基于补丁的标注方法。与传统的基于补丁的标注方法相比，取得了有前景的标注结果，表明该方法在未来临床研究中的潜在应用。
