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Automatic Segmentation of Head and Neck CT Images for Radiotherapy Treatment Planning using Multiple Atlases, Statistical Appearance Models, and Geodesic Active Contours

Institution:
1Department for Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
2Paul Scherrer Institut, Villigen, Switzerland.
3Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy.
4Institute for Biomedical Image Analysis, Private University of Health Sciences, Medical Informatics and Technology, Hall in Tirol 6060, Austria.
Publisher:
American Institute of Physics
Publication Date:
May-2014
Journal:
Med Phys
Volume Number:
41
Issue Number:
5
Pages:
051910
Citation:
Med Phys. 2014 May;41(5):051910.
PubMed ID:
24784389
PMCID:
PMC4000401
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Fritscher K.D., Peroni M., Zaffino P., Spadea M.F., Schubert R., Sharp G. Automatic Segmentation of Head and Neck CT Images for Radiotherapy Treatment Planning using Multiple Atlases, Statistical Appearance Models, and Geodesic Active Contours. Med Phys. 2014 May;41(5):051910. PMID: 24784389. PMCID: PMC4000401.
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PURPOSE: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. METHODS: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. RESULTS: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. CONCLUSIONS: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

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