Toward reliable multi-generational analysis of anatomical trees in 3D high-resolution CT images

Modern micro-CT and multidetector helical CT scanners can produce high-resolution 3D digital images of various anatomical tree structures, such as the coronary or hepatic vasculature and the airway tree. The sheer size and complexity of these trees make it essentially impossible to define them interactively. Automatic approaches, using techniques such as image segmentation, thinning, and centerline definition, have been proposed for a few specific problems. None of these approaches, however, can guarantee extracting geometrically accurate multigenerational tree structures. This limits their utility for detailed quantitative analysis of a tree. This paper proposes an approach for accurately defining 3D trees depicted in large 3D CT images. Our approach utilizes a three-stage analysis paradigm: (1) Apply an automated technique to make a "first cut" at defining the tree. (2) Analyze the automatically defined tree to identify possible errors. (3) Use a series of interactive tools to examine and correct each of the identified errors. At the end of this analysis, in principle, a more useful tree will be defined. Our paper will present a preliminary description of this paradigm and give some early results with 3D micro-CT images.