An iterative Bayesian approach for liver analysis: tumors validation study

We present a new method and validation study for the nearly automatic segmentation of liver tumors. The method is part of a nearly automatic system for simultaneous segmentation of liver contours, vessels, and tumors from abdominal CTA scans. It repeatedly applies multiresolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. The method uses multi-class and voxel neighborhood information to compute an accurate intensity distribution function for each class. Only one user-defined voxel seed for the liver and a few extra seeds for the tumors are required for initialization, without any manual adjustment of internal parameters. A retrospective study on a validated clinical dataset totaling 20 tumors from 9 patients CTAs’ was performed as part of the MICCAI’08 liver tumors segmentation grand-challenge. An aggregated competition score of 61 was obtained on the test set of this database. In addition we measured the robustness of our algorithm to different seeds initializations. These results suggest that our method is clinically applicable, accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-automatic segmentation methods.