A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network
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Abstract (arXiv)
The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a statistical correspondence between the reconstructed data and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 57 references that are in the map. First posted on arXiv 3 Mar 2023.
Builds on
6 of 57 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2022Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographscited by 12
- 2020Learning Hybrid Representations for Automatic 3D Vessel Centerline Extractioncited by 9
- 2020Skeletonization via Local Separatorscited by 6
- 2013Virtual Location-Based Services: Merging the Physical and Virtual Worldcited by 3
- 2019Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagramscited by 1
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Citing papers per year: 2023: 1.
1 paper.