A Flexible, Generic Model for Anatomic Shape: Application to Interactive Two-Dimensional Medical image Segmentation and Matching

Abstract

A representation called a radial contour model (RCM) is described for two-dimensional anatomic shapes. The model, which is a type of a geometric constraint network (GCN), is both flexible, in that it can deform to fit a particular instance of an anatomic shape, and generic, in that it captures all examples of a particular anatomic shape class. The model is implemented in a program, called SCANNER (version 0.7), for interactive model-based two-dimensional image segmentation and matching. Use of the model allows the segmenter to direct the search for edges in the image, and to fill in edges where none are present. Evaluations were done using models of 15 cross-sectional shapes appearing on CT images from 16 patients. Results from 480 trials show that the model-based approach reduces segmentation time by nearly a factor of 3 over manual methods, and correctly classifies 72.9% of the contours. The results not only suggest that the RCM will be useful for several current medical image segmentation tasks, but also support the hypothesis that geometric constraint networks are a viable approach to anatomic shape representation.