2020
DOI: 10.48550/arxiv.2001.07186
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Abstract: In this work, we introduce an algorithmic approach to generate microvascular networks starting from larger vessels that can be reconstructed without noticeable segmentation errors. Contrary to larger vessels, the reconstruction of fine-scale components of microvascular networks shows significant segmentation errors, and an accurate mapping is time and cost intense. Thus there is a need for fast and reliable reconstruction algorithms yielding surrogate networks having similar stochastic properties as the origin… Show more

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Cited by 1 publication
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“…This means that the 1D variables φ v and p v on a 1D vessel Λ i depend only on s i . For further details related to the derivation of 1D pipe flow and transport models, we refer to [29]. Accordingly, the 1D model equations for flow and transport on Λ i read as follows,…”
Section: 4mentioning
confidence: 99%
“…This means that the 1D variables φ v and p v on a 1D vessel Λ i depend only on s i . For further details related to the derivation of 1D pipe flow and transport models, we refer to [29]. Accordingly, the 1D model equations for flow and transport on Λ i read as follows,…”
Section: 4mentioning
confidence: 99%