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.