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MICCAI
2008
Springer

A Bayesian Approach for Liver Analysis: Algorithm and Validation Study

9 years 8 months ago
A Bayesian Approach for Liver Analysis: Algorithm and Validation Study
Abstract. We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels, and metastatic lesions 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. The method requires only one or two user-defined voxel seeds, with no manual adjustment of internal parameters. A retrospective study on two validated clinical datasets totaling 56 CTAs was performed. We obtained correlations of 0.98 and 0.99 with a manual ground truth liver volume estimation for the first and second databases, and a total score of 67.87 for the second database. These results suggest that our method is accurate, efficient, and robust to seed selection compared to manually generated ground truth segmentation and to other semi-a...
Moti Freiman, Ofer Eliassaf, Yoav Taieb, Leo Jo
Added 06 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where MICCAI
Authors Moti Freiman, Ofer Eliassaf, Yoav Taieb, Leo Joskowicz, Jacob Sosna
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