title: A novel method for identifying a graph-based representation of 3-D microvascular networks from fluorescence microscopy image stacks.
abstract: A novel approach to determine the global topological structure of a microvasculature network from noisy and low-resolution fluorescence microscopy data that does not require the detailed segmentation of the vessel structure is proposed here. The method is most appropriate for problems where the tortuosity of the network is relatively low and proceeds by directly computing a piecewise linear approximation to the vasculature skeleton through the construction of a graph in three dimensions whose edges represent the skeletal approximation and vertices are located at Critical Points (CPs) on the microvasculature. The CPs are defined as vessel junctions or locations of relatively large curvature along the centerline of a vessel. Our method consists of two phases. First, we provide a CP detection technique that, for junctions in particular, does not require any a priori geometric information such as direction or degree. Second, connectivity between detected nodes is determined via the solution of a Binary Integer Program (BIP) whose variables determine whether a potential edge between nodes is or is not included in the final graph. The utility function in this problem reflects both intensity-based and structural information along the path connecting the two nodes. Qualitative and quantitative results confirm the usefulness and accuracy of this method. This approach provides a mean of correctly capturing the connectivity patterns in vessels that are missed by more traditional segmentation and binarization schemes because of imperfections in the images which manifest as dim or broken vessels.
authors.affiliation: Dept. Electrical and Computer Engineering, Tufts University, Medford, MA, USA.
authors.forename: Sepideh
authors.initials: S
authors.lastname: Almasi
authors.affiliation: Dept. Radiology, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Xiaoyin
authors.initials: X
authors.lastname: Xu
authors.affiliation: Dept. Neurobiology, Harvard Medical School, Boston, MA, USA.
authors.forename: Ayal
authors.initials: A
authors.lastname: Ben-Zvi
authors.affiliation: Dept. Neurobiology, Harvard Medical School, Boston, MA, USA.
authors.forename: Baptiste
authors.initials: B
authors.lastname: Lacoste
authors.affiliation: Dept. Neurobiology, Harvard Medical School, Boston, MA, USA.
authors.forename: Chenghua
authors.initials: C
authors.lastname: Gu
authors.affiliation: Dept. Electrical and Computer Engineering, Tufts University, Medford, MA, USA.
authors.forename: Eric L
authors.initials: EL
authors.lastname: Miller
mesh_terms: D000818:Animals; D066264:Datasets as Topic; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D055806:Microvessels / Q000033:anatomy & histology*; D008955:Models, Cardiovascular; D012680:Sensitivity and Specificity
keywords: Convex hull; Graph extraction; Junction detection; Microvascular network; Tubular structures