Shape constrained mesh editing for delineating anatomical structures in volumetric datasets

Organ delineation from volumetric dataset is often encountered problem in medical imaging. Numerous 3D polygonal surface mesh model based segmentation algorithms have been reported in this area. These algorithms aim for a fully automated solution for segmenting anatomical structures in volumetric image datasets. But attraction to false boundaries results in inaccurate segmentation, which can happen because of poor model initialization or weak image feature response at correct locations. To be acceptable in clinical practice, it is crucial for a segmentation approach to include the possibility of integrating input from the user when automatic segmentation fails to provide the desired accuracy. This work presents a new 3D triangular surface mesh editing technique to correct the results of automatic segmentation. The method directly projects subset of mesh vertices that are closest to a user drawn line, representing the true boundary of the anatomical structures. Its neighboring vertices that are within user defined depth get deformed based on shape constraints resulting in a smooth edited surface. The shape constraint is formulated to maintain the distribution of the vertices in the region of edited mesh in correspondence with the initial surface model. The result of the study shows that the manually edited surface results in accurate (1.52+/- 0.5mm) anatomical boundary on the edited image plane while preserving the shape of the surface in the neighboring region.