Chapter title |
Atlas-based auto-segmentation of head and neck CT images.
|
---|---|
Chapter number | 52 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2008
|
DOI | 10.1007/978-3-540-85990-1_52 |
Pubmed ID | |
Book ISBNs |
978-3-54-085989-5, 978-3-54-085990-1
|
Authors |
Xiao Han, Mischa S. Hoogeman, Peter C. Levendag, Lyndon S. Hibbard, David N. Teguh, Peter Voet, Andrew C. Cowen, Theresa K. Wolf, Mischa S Hoogeman, Peter C Levendag, Lyndon S Hibbard, David N Teguh, Andrew C Cowen, Theresa K Wolf, Han, Xiao, Hoogeman, Mischa S., Levendag, Peter C., Hibbard, Lyndon S., Teguh, David N., Voet, Peter, Cowen, Andrew C., Wolf, Theresa K. |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 3% |
United Kingdom | 2 | 1% |
Netherlands | 1 | <1% |
Sweden | 1 | <1% |
Switzerland | 1 | <1% |
Iran, Islamic Republic of | 1 | <1% |
Portugal | 1 | <1% |
Denmark | 1 | <1% |
Belgium | 1 | <1% |
Other | 0 | 0% |
Unknown | 147 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 45 | 28% |
Researcher | 29 | 18% |
Student > Master | 19 | 12% |
Student > Bachelor | 14 | 9% |
Professor > Associate Professor | 6 | 4% |
Other | 14 | 9% |
Unknown | 33 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 32 | 20% |
Medicine and Dentistry | 28 | 18% |
Computer Science | 25 | 16% |
Physics and Astronomy | 21 | 13% |
Biochemistry, Genetics and Molecular Biology | 2 | 1% |
Other | 8 | 5% |
Unknown | 44 | 28% |