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Fully automated liver segmentation for low- and high- contrast CT volumes based on probabilistic atlases

Automated liver segmentation is problematic due to variations in liver shape / size and because the liver has a similar density distribution to surrounding structures. We propose a method that: 1) utilizes iteratively constructed probabilistic liver and rib cage atlases, 2) conducts the Gaussian distribution analysis to avoid incorrectly classifying the irrelevant surrounding tissues as ‘liver region’ in the conventional probabilistic atlas based method, and maps the intensity range of the input candidate liver region onto the liver atlas, 3) retrieves the ‘missing parts’ of the liver by deformable registration. Our approach is automated and able to segment the liver from high-contrast and low-contrast CT volumes. Forty clinical CT studies were used for atlas construction and validation. Our method outperformed two other probabilistic atlas-based liver segmentation methods.