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- mid intra-rater variability. To reduce manual labor, we have developed a fully automated, a fully automated, 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 mid robustness while, still being able to account for large inter-subject shape differences. Validation results showed that our method provides accurate segmentation for many structure's despite difficulties presented by real clinical data. Comparison of two different, atlas selection strategies is also reported.