Presentation + Paper
15 March 2019 Automatic quality control using hierarchical shape analysis for cerebellum parcellation
Lianrui Zuo, Shuo Han, Aaron Carass, Sarah H. Ying, Chiadikaobi U. Onyike, Jerry L. Prince
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lianrui Zuo, Shuo Han, Aaron Carass, Sarah H. Ying, Chiadikaobi U. Onyike, and Jerry L. Prince "Automatic quality control using hierarchical shape analysis for cerebellum parcellation", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490J (15 March 2019); https://doi.org/10.1117/12.2512805
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KEYWORDS
Cerebellum

Image segmentation

Shape analysis

Automatic control

Error analysis

Medicine

Visualization

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