EFFICIENT CONSTRUCTION OF STATISTICAL SHAPE MODELS FOR PATIENT-SPECIFIC MODELING

摘要

Statistical Shape Modeling (SSM) is a powerful tool to capture the shape variation pattern across a group of shapes belonging to a certain shape class. SSM has seen many promising applications for the purpose of building Patient-Specific Model (PSM) because it avoids unwanted exposure to ionizing radiation from imaging modalities such as CT scanning and has potential as a cost-saving and time-efficient research and clinical tool. All that is needed to reconstruct the patient - specific data is to instantiate the statistical model already generated. The utility of the statistical model relies on a sufficiently large training set data pool from as many patients as possible; and more importantly, a reasonably good correspondence across the entire training set. As such, the description length has been used as a standard to measure the quality of correspondence and the statistical model, and the desired correspondence found by optimization.