@inproceedings{9b6435feafd24255a851491d610368bd,
title = "Automatic quality control using hierarchical shape analysis for cerebellum parcellation",
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.",
keywords = "cerebellum segmentation, quality control, shape analysis",
author = "Lianrui Zuo and Shuo Han and Aaron Carass and Ying, {Sarah H.} and Onyike, {Chiadikaobi U.} and Prince, {Jerry L.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2019: Image Processing ; Conference date: 19-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2512805",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Angelini, {Elsa D.} and Angelini, {Elsa D.} and Angelini, {Elsa D.} and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2019",
}