The field of computed tomography (CT) has disclosed a number of techniques for the three-dimensional segmentation of the liver. However, MR images have a different image quality and generally show other tissue properties than CT images. MR images have less pronounced edge structures, which makes the segmentation more difficult. There are additional complications in the segmentation of MR images as a result of image artifacts and partial volume effects.
Only a few methods have been disclosed to date for the three-dimensional segmentation of the liver from volume data records from magnetic resonance imaging. Thus, for example, K. Cheng et al. “A Novel Shape Prior Based Level Set Method for Liver Segmentation from MR Images”, Proc. of the 5th Int. Conf. on Information Technology and Applications in Biomedicine, 2008, pages 144 to 147 present a method in which the liver is segmented on the basis of a 3D liver shape model from a single-channel MRI volume data record. However, the three-dimensional shape of the liver varies greatly in reality, and so reliable segmentation using such an approach may cause difficulties. Furthermore, this technique does not allow fully-automatic segmentation of the liver either, the latter being desirable for a plurality of applications. Thus, by way of example, epidemiological questions from modern research should be processed, in which the liver volume of a large number of subjects must be determined from the volume data records. As a result of the large number of data records, such an epidemiological evaluation can hardly be performed using a manual segmentation of the liver due to the high time expenditure.