Deformable Models for Biomedical Data Analysis

The rapid development of computer and noninvasive sensor technologies is revolutionizing medicine, offering scientists and physicians powerful new investigative and diagnostic tools. Computer-based medical image analysis is an especially challenging task which has not kept pace with the ability to acquire digital images in various modalities. In response to this challenge, we have been developing several new segmentation, registration, shape reconstruction, and motion tracking techniques for multidimensional medical image analysl~s. Our work exploits deformable models. Deformable models offer a fundamentally dynamic approach to nonrigid shape and motion analysis based on computational physics. Typically, the models are governed by the Lagrangian mechanics of elastic media, and they are coupled to various data sets (and they may be manipulated interactively) thorough force fields. Although they were originally developed in the context of computer vision [1] and computer graphics [2], deformable models are also naturally applicable to biomedical data analysis. This is because the human body is a complex, highly deformable structure. In this summary, I will not attempt to survey the growing cavalcade of research on deformable models in medical image analysis (see, e.g., the forthcoming collection [3]). I will, however, review some of our recent work which applies deformable models to a variety of static and time varying medical data sets from scales on the macroscopic to the microscopic. The subsequent sections focus on the extraction of 3D models from serial microscopy, opthalmic image analysis, dynamic 3D cardiac image analysis, and tracking for biomechanics. The details are available in the references.