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Automatic quality control using hierarchical shape analysis for cerebellum parcellation
Author(s): Lianrui Zuo; Shuo Han; Aaron Carass; Sarah H. Ying; Chiadikaobi U. Onyike; Jerry L. Prince
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Paper Abstract

Automatic and accurate cerebellum parcellation has long been a challenging task due to the relative surface complexity and large anatomical variation of the human cerebellum. An inaccurate segmentation will inevitably bias further studies. In this paper we present an automatic approach for the quality control of cerebellum parcellation based on shape analysis in a hierarchical structure. We assume that the overall shape variation of a 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 level shape captures both population and segmentation variation. We use a partial least squares regression to combine the lower level and higher level shape information. By compensating for population variation, we show that the estimated 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 about when a segmentation software may fail in real scenarios.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490J (15 March 2019); doi: 10.1117/12.2512805
Show Author Affiliations
Lianrui Zuo, Johns Hopkins Univ. (United States)
Shuo Han, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Sarah H. Ying, Johns Hopkins Univ. (United States)
Chiadikaobi U. Onyike, Johns Hopkins Univ. (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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