A program called SCANNER (version 0.6) is described for performing 2-D interactive medical image segmentation using knowledge of anatomic shape. The knowledge is implemented in a radial contour model, which is a flexible, generic model that can accurately deform to fit the data, but which also encodes the expected shape and range of variation for a 2-D contour shape class. The model, which can describe contours that are single-valued distortions of a circle, is learned from training sets of similarly-shaped contours. Variation in the learned model allows it to provide search regions for low level edge detectors, thereby reducing the incidence of false edges. Initial evaluation of this system was performed for structures seen in 111 2-D CT images from 12 patients undergoing radiation treatment planning for cancer. The results suggest that the model is able to capture the cross-sectional expected shape and range of variation for several clinically-important structures (the liver, kidney, eye, and some tumors), that the knowledge-based approach should reduce the segmentation time over current manual methods by a factor between two and ten, and that the usefulness of the model decreases as variability of the structure increases.