The paper studies the feasibility of using 3D extensions of two state-of-the-art segmentation techniques, the Statistical Region Merging (SRM) method and the Efficient Graph-based Segmentation (EGS) technique, for automatic anatomy segmentation on clinical 3D CT images. The proposed methods are tested on a dataset of 55 images. The test is for segmentation of eight representative tissues (lungs, stomach, liver, heart, kidneys, spleen, bones and the spinal cord) which are vital for accurate calculation of radiation doses. The results are evaluated using the Dice index, the Hausdorff distance and the H t index, a measure of border error with tolerance t pixels addressing the uncertainty in the ground truth. The outcome shows that the 3D-SRM method outperforms 3D-EGS and has a great potential to become the method of choice for segmentation of full-body CT images. Using 3D-SRM, the average Dice index, the Hausdorff distance across the 8 tissues, and the H 2 were 0.89, 12.5 mm and 0.93, respectively.