Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

• Type of Material: Article
• Authors: Xiaofeng Yang, Emory University; Ning Wu, Jilin University; Guanghui Cheng, Jilin University; Zhengyang Zhou, Nanjing University; David Yu, Emory University; Jonathan J Beitler, Emory University; Walter J Curran, Emory University; Tian Liu, Emory University
• Language: English
• Date: 2014-12-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ijrobp.2014.08.350
• Title of Journal or Parent Work: International Journal of Radiation Oncology - Biology - Physics
• ISSN: 0360-3016
• Volume: 90
• Issue: 5
• Start Page: 1225
• End Page: 1233
• Grant/Funding Information: Drs. Guanghui Cheng and Ning Wu were supported in part by the National Natural Science Foundation of China (81201737); the Natural Science Foundation of Jilin Province (20090458, 201015183); the Jilin University Fundamental Research Project (201103055); and the Young Scholars Research Foundation Program of China-Japan Union Hospital (2009).; Dr Xiaofeng Yang was supported in part by the US Department of Defense Research grant W81XWH-13-1-0269.
• Keywords: VOLUME; Radiology, Nuclear Medicine & Medical Imaging; CT IMAGES; Oncology; AUTO-SEGMENTATION; APPEARANCE; RETRIEVAL; RADIOTHERAPY; SUBMANDIBULAR-GLANDS; Science & Technology; XEROSTOMIA; Life Sciences & Biomedicine; FEATURES; CANCER
• Research Categories: Physics, Radiation; Health Sciences, Radiology; Health Sciences, Oncology

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.