Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

Background Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing da...

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Veröffentlicht in:	BMC bioinformatics Jg. 22; H. 1; S. 346 - 28
Hauptverfasser:	Drees, Dominik, Scherzinger, Aaron, Hägerling, René, Kiefer, Friedemann, Jiang, Xiaoyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 26.06.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Anisotropy Bioinformatics Biomedical and Life Sciences Biomedical research Branches Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Data mining Datasets Feature extraction Graph extraction Graph theory Graphical representations Image Processing, Computer-Assisted Image segmentation Imaging and image analysis Imaging, Three-Dimensional Iterative methods Large volume processing Life Sciences Methodology Methodology Article Methods Microarrays Microscopy Morphology Network analysis Network topologies Neural networks Random access memory Robustness (mathematics) Scalability Topology Vessel network Germany Vessel network Graph extraction Large volume processing Scalability
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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