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A comprehensive understanding of microvascular networks is required to generate platforms that track treatment efficacy. Current approaches are labor intensive and limited to small tissue volumes. In this work we describe an acquisition and segmentation framework for low-cost imaging and microvascular modeling at resolution comparable to confocal with data rates comparable to light-sheet microscopy. Segmentation is performed using a GPU-based method that extracts microvascular structure and connectivity embedded in these images. The microvasculature network is stored such that the graph [G = V, E] structure can be exploited to quantify large-scale angiomes and facilitate data mining at the terabyte scale.
Pavel Govyadinov,Jiaming Guo,Leila Saadatifard,Camille Artur,Jason Eriksen,Guoning Chen, andDavid Mayerich
"Reconstructing the angiome: a new platform for characterizing disease progression and drug efficacy", Proc. SPIE 11231, Design and Quality for Biomedical Technologies XIII, 112310B (17 February 2020); https://doi.org/10.1117/12.2555137
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Pavel Govyadinov, Jiaming Guo, Leila Saadatifard, Camille Artur, Jason Eriksen, Guoning Chen, David Mayerich, "Reconstructing the angiome: a new platform for characterizing disease progression and drug efficacy," Proc. SPIE 11231, Design and Quality for Biomedical Technologies XIII, 112310B (17 February 2020); https://doi.org/10.1117/12.2555137