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.
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