We review and discuss different classes of image segmentation methods. The usefulness of these methods is illustrated by a number of clinical cases. Segmentation is the process of assigning labels to pixels in 2D images or voxels in 3D images. Typically the effect is that the image is split up into segments, also called regions or areas. In medical imaging it is essential for quantification of outlined structures and for 3D visualization of relevant image data. Based on the level of implemented model knowledge we have classified these methods into (1) manual delineation, (2) low-level segmentation, and (3) model-based segmentation. Pure manual delineation of structures in a series of images is time-consuming and user-dependent and should therefore be restricted to quick experiments. Low-level segmentation analyzes the image locally at each pixel in the image and is practically limited to high-contrast images. Model-based segmentation uses knowledge of object structure such as global shape or semantic context. It typically requires an initialization, for example in the form of a rough approximation of the contour to be found. In practice it turns out that the use of high-level knowledge, e.g. anatomical knowledge, in the segmentation algorithm is quite complicated. Generally, the number of clinical applications decreases with the level and extent of prior knowledge needed by the segmentation algorithm. Most problems of segmentation inaccuracies can be overcome by human interaction. Promising segmentation methods for complex images are therefore user-guided and thus semi-automatic. They require manual intervention and guidance and consist of fast and accurate refinement techniques to assist the human operator.