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Proceedings Paper

Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy
Author(s): Guanghui Cheng; Xiaofeng Yang; Ning Wu; Zhijian Xu; Hongfu Zhao; Yuefeng Wang; Tian Liu
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Paper Abstract

Xerostomia (dry mouth), resulting from radiation damage to the parotid glands, is one of the most common and distressing side effects of head-and-neck cancer radiotherapy. Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. In the clinic, parotid-volume evaluation is exclusively based on physicians’ manual contours. However, manual contouring is time-consuming and prone to inter-observer and intra-observer variability. Here, we report a fully automated multi-atlas-based registration method for parotid-gland delineation in 3D head-and-neck MR images. The multi-atlas segmentation utilizes a hybrid deformable image registration to map the target subject to multiple patients’ images, applies the transformation to the corresponding segmented parotid glands, and subsequently uses the multiple patient-specific pairs (head-and-neck MR image and transformed parotid-gland mask) to train support vector machine (SVM) to reach consensus to segment the parotid gland of the target subject. This segmentation algorithm was tested with head-and-neck MRIs of 5 patients following radiotherapy for the nasopharyngeal cancer. The average parotid-gland volume overlapped 85% between the automatic segmentations and the physicians’ manual contours. In conclusion, we have demonstrated the feasibility of an automatic multi-atlas based segmentation algorithm to segment parotid glands in head-and-neck MR images.

Paper Details

Date Published: 28 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702Q (28 February 2013); doi: 10.1117/12.2007783
Show Author Affiliations
Guanghui Cheng, China-Japan Union Hospital of Jilin Univ. (China)
Xiaofeng Yang, Winship Cancer Institute, Emory Univ. (United States)
Ning Wu, China-Japan Union Hospital of Jilin Univ. (China)
Zhijian Xu, China-Japan Union Hospital of Jilin Univ. (China)
Hongfu Zhao, China-Japan Union Hospital of Jilin Univ. (China)
Yuefeng Wang, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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