title: Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.
abstract: OBJECTIVE
  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
  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.
authors.affiliation: Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Yang
authors.affiliation: Radiation Oncology, Jilin University, Chuangchun, Jilin, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Wu
authors.affiliation: Radiation Oncology, Jilin University, Chuangchun, Jilin, China.
authors.forename: Guanghui
authors.initials: G
authors.lastname: Cheng
authors.affiliation: Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
authors.forename: Zhengyang
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia.
authors.forename: David S
authors.initials: DS
authors.lastname: Yu
authors.affiliation: Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia.
authors.forename: Jonathan J
authors.initials: JJ
authors.lastname: Beitler
authors.affiliation: Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia.
authors.forename: Walter J
authors.initials: WJ
authors.lastname: Curran
authors.affiliation: Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia. Electronic address: tliu34@emory.edu.
authors.forename: Tian
authors.initials: T
authors.lastname: Liu
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D005583:Fourier Analysis; D006258:Head and Neck Neoplasms / Q000532:radiotherapy*; D006801:Humans; D007822:Laryngeal Neoplasms / Q000532:radiotherapy; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008488:Medical Illustration; D008875:Middle Aged; D015588:Observer Variation; D009929:Organ Size / Q000528:radiation effects; D010306:Parotid Gland / Q000033:anatomy & histology* / Q000473:pathology / Q000528:radiation effects*; D011829:Radiation Dosage; D014987:Xerostomia / Q000209:etiology*