Share Email Print

Proceedings Paper

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region
Author(s): Claudia Leavens; Torbjørn Vik; Heinrich Schulz; Stéphane Allaire; John Kim; Laura Dawson; Brian O'Sullivan; Stephen Breen; David Jaffray; Vladimir Pekar
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 26 March 2008
PDF: 8 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69143G (26 March 2008); doi: 10.1117/12.769710
Show Author Affiliations
Claudia Leavens, Univ. of Toronto (Canada)
Torbjørn Vik, Philips Research Europe (Germany)
Heinrich Schulz, Philips Research Europe (Germany)
Stéphane Allaire, Univ. of Toronto (Canada)
John Kim, Univ. of Toronto (Canada)
Laura Dawson, Univ. of Toronto (Canada)
Brian O'Sullivan, Univ. of Toronto (Canada)
Stephen Breen, Univ. of Toronto (Canada)
David Jaffray, Univ. of Toronto (Canada)
Vladimir Pekar, Philips Research North America (Canada)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

© SPIE. Terms of Use
Back to Top