This work presents first quantitative results of a method for automatic liver segmentation from CT data. It is based on a 3D deformable model approach using a-priori statistical information about the shape of the liver gained from a training set. The model is adapted to the data in an iterative process by analysis of the grey value profiles along its surface normals after nonlinear diffusion filtering. Leave-one-out experiments over 26 CT data sets reveal an accuracy of 2.4 mm with respect to the manual segmentation.