System for the analysis and visualization of large 3D anatomical trees

Abstract

Modern micro-CT and multi-detector helical CT scanners can produce high-resolution 3D digital images of various anatomical trees. The large size and complexity of these trees make it essentially impossible to define them interactively. Automatic approaches have been proposed for a few specific problems, but none of these approaches guarantee extracting geometrically accurate multi-generational tree structures. This paper proposes an interactive system for defining and visualizing large anatomical trees and for subsequent quantitative data mining. The system consists of a large number of tools for automatic image analysis, semi-automatic and interactive tree editing, and an assortment of visualization tools. Results are presented for a variety of 3D high-resolution images.

Combined video tracking and image-video registration for continuous bronchoscopic guidance

“…These include methods for airway-tree segmentation, surface definition (Marching Cubes), and route calculation [23,[37][38][39][40][41][42]. The surface information is used throughout guided bronchoscopy to produce airway endoluminal renderings.…”

On the complexity of finding a largest common subtree of bounded degree

“…In order to find a common structure between two trees, extensive studies have been done on finding a largest common subtree (LCST) 1 based on a bijective mapping between subsets of nodes of the two input trees which preserves labels and ancestry relationship, a mapping which is intimately related to the edit distance problem for rooted trees [23]. The LCST and related problems have various applications in bioinformatics including comparison of glycans [5], vascular networks [21], and cell lineage data [12]. They also have applications in comparison and search of XML data [13] and documents processed by natural language processing [20].…”

“…They also have applications in comparison and search of XML data [13] and documents processed by natural language processing [20]. In many applications, it is required or desirable to treat input trees as unordered trees rather than ordered trees because the ordering of children is not uniquely determined in many cases [5,12,13,20,21].…”

Interactive Deformable Registration Visualization and Analysis of 4D Computed Tomography

“…Anything with a tree-like structure, such as the branches of the lungs or veins of the liver, is certainly a challenge as well. A system of dealing with tree-like structure in general was suggested recently by Yu, Ritman, and Higgins which moves away from interactive segmentation and instead facilitates "semi-automatic" analysis by first extracting the tree structure automatically and then offering the ability to edit it, basing all visualizations off of that result [57].…”