3D image segmentation by using statistical deformation models and level sets

Purpose Improved segmentation of soft objects was sought using a new method that combines level set segmentation with statistical deformation models, using prior knowledge of the shape of an object as well as information derived from the input image. Methods Statistical deformation models were created using Euclidian distance functions of binary data and a multi-hierarchical registration approach based on mutual information metric and demons deformable registration. This approach is motivated by the fact that models based on signed distance maps, traditionally combined with level set segmentation can result in irregular shapes and do not establish explicit correspondences. By using statistical deformation models as representation of shape and a maximum a posteriori (MAP) estimation model to estimate the MAP shape of the object to be segmented, a robust segmentation algorithm using accurate shape models could be developed. Results The accuracy and correctness of the synthesized models was evaluated on different 3D objects (cardiac MRI and spinal CT vertebral segment) and the segmentation algorithm was validated by performing different segmentation tasks using various image modalities. The results of this evaluation are very promising and show the potential utility of the approach. Conclusion Initial results demonstrate the approach is feasible and may be advantageous over alternative segmentation methods. Extensions of the model, which also incorporate prior knowledge about the spatial distribution of grey values, are currently under development.