Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Issue 5 (24th April 2014)

• Record Type: Journal Article
• Authors: Fritscher, Karl D.; Peroni, Marta; Zaffino, Paolo; Spadea, Maria Francesca; Schubert, Rainer; Sharp, Gregory
• Is Part Of: Medical physics. Volume 41:Issue 5(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-04-24
• Subjects: Computed tomography -- Segmentation -- Therapeutic applications, including brachytherapy -- Registration -- Artifacts and distortion -- Contrast; brain -- computerised tomography -- differential geometry -- image fusion -- image registration -- image segmentation -- iterative methods -- medical image processing -- radiation therapy -- statistical analysis; radiation therapy planning -- atlas‐based segmentation -- geodesic active contours -- InShape models; Computerised tomographs -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Medical imaging -- Geodesy -- Medical image segmentation -- Computed tomography -- Geoinformatics -- Anatomy -- Statistical model calculations -- Medical image contrast -- Image registration -- Intensity modulated radiation therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4871623
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.