Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy

Guanghui Cheng; Xiaofeng Yang; Ning Wu; Zhijian Xu; Hongfu Zhao; Yuefeng Wang; Tian Liu

doi:10.1117/12.2007783

28 February 2013 Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy

Guanghui Cheng, Xiaofeng Yang, Ning Wu, Zhijian Xu, Hongfu Zhao, Yuefeng Wang, Tian Liu

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86702Q (2013) https://doi.org/10.1117/12.2007783
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Guanghui Cheng, Xiaofeng Yang, Ning Wu, Zhijian Xu, Hongfu Zhao, Yuefeng Wang, and Tian Liu "Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702Q (28 February 2013); https://doi.org/10.1117/12.2007783

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