Fully Automatic Liver Volumetry Using 3D Level Set Segmentation for Differentiated Liver Tissue Types in Multiple Contrast MR Datasets

Modern epidemiological studies analyze a high amount of magnetic resonance imaging (MRI) data, which requires fully automatic segmentation methods to assist in organ volumetry. We propose a fully automatic two-step 3D level set algorithm for liver segmentation in MRI data that delineates liver tissue on liver probability maps and uses a distance transform based segmentation refinement method to improve segmentation results. MR intensity distributions in test subjects are extracted in a training phase to obtain prior information on liver, kidney and background tissue types. Probability maps are generated by using linear discriminant analysis and Bayesian methods. The algorithm is able to differentiate between normal liver tissue and fatty liver tissue and generates probability maps for both tissues to improve the segmentation results. The algorithm is embedded in a volumetry framework and yields sufficiently good results for use in epidemiological studies.