ArticleIEEE transactions on visualization and computer graphics2019
Robust Tracing and Visualization of Heterogeneous Microvascular Networks.
Article in IEEE transactions on visualization and computer graphics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction.Frontiers in neuroinformatics · 2026Article
- Segmentation and modeling of large-scale microvascular networks: a survey.Frontiers in bioinformatics · 2025Review
- Three-Dimensional Microscopy by Milling with Ultraviolet Excitation.Scientific reports · 2019Article
- Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow.Analytical methods : advancing methods and applications · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
Advances in high-throughput imaging allow researchers to collect three-dimensional images of whole organ microvascular networks. These extremely large images contain networks that are highly complex, time consuming to segment, and difficult to visualize. In this paper, we present a framework for segmenting and visualizing vascular networks from terabyte-sized three-dimensional images collected using high-throughput microscopy. While these images require terabytes of storage, the volume devoted to the fiber network is ≈ 4 percent of the total volume size. While the networks themselves are sparse, they are tremendously complex, interconnected, and vary widely in diameter. We describe a parallel GPU-based predictor-corrector method for tracing filaments that is robust to noise and sampling errors common in these data sets. We also propose a number of visualization techniques designed to convey the complex statistical descriptions of fibers across large tissue sections-including commonly studied microvascular characteristics, such as orientation and volume.
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Registered trials
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