Rapid, accurate and reproducible delineation and measurement of arbitrary anatomical structures in medical images is a widely held goal, with important applications in both clinical diagnostics and, perhaps more significantly, pharmaceutical trial evaluation. This process requires the ability first to localize a structure within the body, and then to find a best approximation of the structure’s boundaries within a given scan. Structures that are tortuous and small in cross section, such as the hippocampus in the brain or the abdominal aorta, present a particular challenge. Their apparent shape and position can change significantly from slice to slice, and accurate prior shape models for such structures are often difficult to form. In this work, we have developed a system that makes use of both a user-defined shape model and a statistical maximum likelihood classifier to identify and measure structures of this sort in MRI and CT images. Experiments show that this system can reduce analysis time by 75% or more with respect to manual tracing with no loss of precision or accuracy.