Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

Claudia Leavens; Torbjørn Vik; Heinrich Schulz; Stéphane Allaire; John Kim; Laura Dawson; Brian O'Sullivan; Stephen Breen; David Jaffray; Vladimir Pekar

doi:10.1117/12.769710

26 March 2008 Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

Claudia Leavens, Torbjørn Vik, Heinrich Schulz, Stéphane Allaire, John Kim, Laura Dawson, Brian O'Sullivan, Stephen Breen, David Jaffray, Vladimir Pekar

Author Affiliations +

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69143G (2008) https://doi.org/10.1117/12.769710
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 ± 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

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Claudia Leavens, Torbjørn Vik, Heinrich Schulz, Stéphane Allaire, John Kim, Laura Dawson, Brian O'Sullivan, Stephen Breen, David Jaffray, and Vladimir Pekar "Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143G (26 March 2008); https://doi.org/10.1117/12.769710

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