A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning
We proposed an homotopy-based SSC to model shape priors accurately and efficiently. The computational efficiency was proportional to the sparsity of a problem. The runtime increased very slowly when training data rose to a large scale. More training shapes and vertex numbers can improve accuracy, without losing speed. Our method can update the optimal solution on the fly.
shape prior sparse shape composition segmentation fast optimization scalability