An iterative Bayesian approach for liver analysis: tumors validation study

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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.

plus my review by Xiang Deng on 07-25-2008 for revision #1
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Categories: Bayesian Decision Theory, Segmentation
Keywords: Liver segmentation, tumor segmentation
Toolkits: ITK
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