An iterative Bayesian approach for liver analysis: tumors validation study

Abstract

We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and tumors from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class smoothed Bayesian classification followed by morphological adjustment and active contours refinement. It 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 additional seeds according to the number of tumors inside the liver are required for initialization. The algorithm do not require manual adjustment of internal parameters. In this work, a retrospective study on a validated clinical dataset totaling 20 tumors from 9 patients CTAs� was performed. 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.