Automatic Quality Control Using Hierarchical Shape Analysis for Cerebellum Parcellation

摘要

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.