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Automatic Quality Control Using Hierarchical Shape Analysis for Cerebellum Parcellation

机译：用于小脑局的分层形状分析的自动质量控制

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Automatic and accurate cerebellum parcellation has long been a challenging task due to the relative surfacecomplexity and large anatomical variation of the human cerebellum. An inaccurate segmentation will inevitablybias further studies. In this paper we present an automatic approach for the quality control of cerebellumparcellation based on shape analysis in a hierarchical structure. We assume that the overall shape variation ofa segmented structure comes from both population and segmentation variation. In this hierarchical structure,the higher level shape mainly captures the population variation of the human cerebellum, while the lower levelshape captures both population and segmentation variation. We use a partial least squares regression to combinethe lower level and higher level shape information. By compensating for population variation, we show that theestimated segmentation variation is highly correlated with the accuracy of the cerebellum parcellation results,which not only provides a confidence measurement of the cerebellum parcellation, but also gives some clues aboutwhen a segmentation software may fail in real scenarios.
机译：由于相对表面，自动和准确的小脑局长长期以来一直是一个具有挑战性的任务人体小脑的复杂性和大的解剖学变异。不准确的细分将不可避免地偏见进一步研究。在本文中，我们提出了一种用于小脑质量控制的自动方法基于层级结构的局部分析。我们假设整体形状变化分段结构来自人口和分割变化。在这个层次结构中，较高水平的形状主要捕获人体小脑的种群变异，而较低水平形状捕获群体和分割变化。我们使用部分最小二乘回归来组合较低级别和更高级别的形状信息。通过赔偿人口变异，我们表明了估计的分割变化与细胞局部结果的准确性高度相关，这不仅提供了小脑局的置信度，还提供了一些关于的线索当分段软件可能在真实方案中失败时。

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