title: Visualization of cellular and microvascular relationships.
abstract: Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.
authors.affiliation: Department of Computer Science, Texas A&M University. mayerichd@neo.tamu.edu
authors.forename: David M
authors.initials: DM
authors.lastname: Mayerich
authors.forename: Louise
authors.initials: L
authors.lastname: Abbott
authors.forename: John
authors.initials: J
authors.lastname: Keyser
mesh_terms: D000465:Algorithms; D001808:Blood Vessels / Q000166:cytology* / Q000502:physiology*; D002468:Cell Physiological Phenomena / Q000502:physiology*; D003196:Computer Graphics; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008833:Microcirculation / Q000502:physiology*; D008853:Microscopy / Q000379:methods*; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity