Knowledge-Driven Ultrasonic Three-Dimensional Organ Modeling

A representation is described for nonstructured biologic objects which are single-valued distortions of a sphere. The representation is implemented in a model-driven system for extracting three-dimensional (3-D) organ reconstructions from a series of arbitrarily oriented ultrasound slices. A training set of ultrasonic reconstructions of similarly shaped objects is used to give the computer generic knowledge of a given shape class. This knowledge is in the form of local slope constraints defined on an object coordinate system. The combination of constraints, interacting together via a relaxation process on continuous label sets, attempts to capture the essential shape and range of variation for an organ class. An initial tolerance region and ``bestguess'' organ surface are established by the interaction of the learned shape knowledge with manually input organ landmarks. A hypothesize-verify paradigm is employed to alternately request new data and to update the tolerance region and bestguess surface. Examples from runs on two balloon classes are presented. These examples show: 1) the local constraints interact to produce a reasonable global depiction of the essential shape and range of variation, 2) the use of shape knowledge permits accurate results from only one third of the available data, and 3) the 3-D shape knowledge provides a two-dimensional (2-D) tolerance region for plan-guided edge detection.