Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. It is evaluated on the task of segmenting tibial contours in knee radiographs. Results are presented using various features, distance weighted K−nearest neighbours and differing eigenspace shape constraints.