Delineation of tumor and organs at risk on each phase of 4D CT images is an essential step in adaptive radiotherapy planning. Manual contouring of the large amount of data is time-consuming and impractical. (Semi-) automated methods typically rely on deformable image registration techniques to automatically map the manual contours drawn in one image to all the other phases in order to get complete 4D contouring, a procedure known as automatic re-contouring. Disadvantages of such approaches are that the manual contouring information is not used in the registration process and the whole volume registration is highly inefficient. In this work, we formulate the automatic re-contouring in a deformable surface model framework, which effectively restricts the computation to a lower dimensional space. The proposed framework was inspired by the morphing active contour model proposed by Bertalmio et al. [1], but we address some limitations of the original method. First, a surface-based regularization is introduced to improve robustness with respect to noise. Second, we design a multi-resolution approach to further improve computational efficiency and to account for large deformations. Third, discrete meshes are used to represent the surface model instead of the implicit level set framework for better computational speed and simpler implementation. Experiment results show that the new morphing active surface model method performs as accurately as a volume registration based re-contouring method but is nearly an order of magnitude faster. The new formulation also allows easy combination of registration and segmentation techniques for further improvement in accuracy and robustness.

Date Published: 8 March 2007
PDF: 9 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123H (8 March 2007); doi: 10.1117/12.709848

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)