Thumbnail
Access Restriction
Open

Author Yang, Xiaofeng ♦ Wu, Ning ♦ Cheng, Guanghui ♦ Zhou, Zhengyang ♦ Yu, David S. ♦ Beitler, Jonathan J. ♦ Curran, Walter J. ♦ Liu, Tian
Source United States Department of Energy Office of Scientific and Technical Information
Content type Text
Language English
Subject Keyword RADIOLOGY AND NUCLEAR MEDICINE ♦ ANATOMY ♦ COMPARATIVE EVALUATIONS ♦ GLANDS ♦ HEAD ♦ IMAGE PROCESSING ♦ LEARNING ♦ NECK ♦ NMR IMAGING ♦ PATIENTS ♦ RADIATION MONITORS ♦ RADIOTHERAPY ♦ RESPIRATION
Abstract Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.
ISSN 03603016
Educational Use Research
Learning Resource Type Article
Publisher Date 2014-12-01
Publisher Place United States
Journal International Journal of Radiation Oncology, Biology and Physics
Volume Number 90
Issue Number 5


Open content in new tab

   Open content in new tab