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- W2794803201 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." @default.
- W2794803201 created "2018-04-06" @default.
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- W2794803201 date "2019-04-01" @default.
- W2794803201 modified "2023-10-16" @default.
- W2794803201 title "Robust Tracing and Visualization of Heterogeneous Microvascular Networks" @default.
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- W2794803201 doi "https://doi.org/10.1109/tvcg.2018.2818701" @default.
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