Atlas-based auto-segmentation of head and neck CT images.
Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of many structures and lymph node regions. Manual contouring is tedious and suffers from large inter- and intra-rater variability. To reduce manual labor, we have developed a fully automated, atlas-based method for H&N CT image segmentation that employs a novel hierarchical atlas registration approach. This registration strategy makes use of object shape information in the atlas to help improve the registration efficiency and robustness while still being able to account for large inter-subject shape differences. Validation results showed that our method provides accurate segmentation for many structures despite difficulties presented by real clinical data. Comparison of two different atlas selection strategies is also reported.
Han
Xiao
X
CMS, Inc., 1145 Corporate Lake Drive, St. Louis, MO 63132, USA.
Hoogeman
Mischa S
MS
Levendag
Peter C
PC
Hibbard
Lyndon S
LS
Teguh
David N
DN
Voet
Peter
P
Cowen
Andrew C
AC
Wolf
Theresa K
TK
D000465:Algorithms; D001185:Artificial Intelligence; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*