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A generative probability model of joint label fusion for multi-atlas based brain segmentation

机译：基于多图集的脑分割的联合标签融合的生成概率模型

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摘要

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.
机译：在许多神经科学研究中，自动标记医学图像中的解剖结构非常重要。最近，已广泛研究了基于补丁的标记，以减轻将地图集注册到目标图像时可能出现的未对准问题。然而，用于注册融合图谱的标签融合的权重通常是独立计算的，因此缺乏防止歧义的图谱补丁促进标签融合的能力。更重要的是，这些权重通常仅基于简单的补丁相似度来计算，因此不一定为标签融合提供最佳解决方案。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集场景中的标签融合过程，目的是通过最佳代表性图集补丁在目标图像中标记每个点，该图集也具有最大的标签一致度。正确标记基础点。具体地，稀疏性约束被施加在标签融合权重上，以便选择最能代表基础目标补丁的少量地图集补丁，从而降低了包括误导性地图集补丁的风险。地图集补丁之间的标签一致是通过探索它们之间的依赖关系来实现的，其中，我们通过分析它们的形态错误模式的相关性以及地图集之间的标签共识，将这些依赖关系建模为正确预测标签中每对地图集补丁的联合概率。 。修补程序依赖项将根据最新的标记结果进一步递归更新，以纠正可能的标记错误，该错误属于期望最大化（EM）框架。为了证明标签的性能，我们对全脑碎裂和海马区分开来了基于补丁的标签方法进行了全面评估。与传统的基于贴片的标记方法相比，已经获得了有希望的标记结果，表明了该方法在未来临床研究中的潜在应用。

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