Automatic graph-based modeling of brain microvessels captured with two-photon microscopy
Files
Accepted manuscript
Date
2019-11
Authors
Damseh, Rafat
Pouliot, Philippe
Gagnon, Louis
Sakadzic, Sava
Boas, David A.
Cheriet, Farida
Lesage, Frederic
Version
OA Version
Published version
Citation
Rafat Damseh, Philippe Pouliot, Louis Gagnon, Sava Sakadzic, David Boas, Farida Cheriet, Frederic Lesage. 2019. "Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy.." IEEE J Biomed Health Inform, Volume 23, Issue 6, pp. 2551 - 2562. https://doi.org/10.1109/JBHI.2018.2884678
Abstract
Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 μm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.
Description
Published in final edited form as: IEEE J Biomed Health Inform. 2019 November ; 23(6): 2551–2562.