Atlas-Based Auto-segmentation of Head and Neck CT Images

Authors:
Xiao Han;Mischa S. Hoogeman;Peter C. Levendag;Lyndon S. Hibbard;David N. Teguh;Peter Voet;Andrew C. Cowen;Theresa K. Wolf
Affiliations:
CMS, Inc., Louis, USA MO 63132;Erasmus Medical Center --- Daniel den Hoed, Rotterdam, The Netherlands;Erasmus Medical Center --- Daniel den Hoed, Rotterdam, The Netherlands;CMS, Inc., Louis, USA MO 63132;Erasmus Medical Center --- Daniel den Hoed, Rotterdam, The Netherlands;Erasmus Medical Center --- Daniel den Hoed, Rotterdam, The Netherlands;CMS, Inc., Louis, USA MO 63132;CMS, Inc., Louis, USA MO 63132
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

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.