Damseh, RafatPouliot, PhilippeGagnon, LouisSakadzic, SavaBoas, David A.Cheriet, FaridaLesage, Frederic2020-11-032020-11-032019-11Rafat 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.2018.28846782168-2208https://hdl.handle.net/2144/41575Published in final edited form as: IEEE J Biomed Health Inform. 2019 November ; 23(6): 2551–2562.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.p. 2551 - 2562en-USAlgorithmsAnimalsBrainDeep learningImage processing, computer-assistedMiceMicroscopy, fluorescence, multiphotonMicrovesselsCerebral microvasculatureConvolution neural networksSegmentationGraphTwo-photon microscopyArticle10.1109/JBHI.2018.28846780000-0002-6709-7711 (Boas, David)467960