Voronoi Tessellation
Voronoi Tessellation - Related Videos
07:49
Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels
0 Views •
2021
Engineered tissues heavily rely on proper vascular networks to provide vital nutrients and gases and remove metabolic waste. In this work, a stepwise seeding protocol of endothelial cells and support cells creates highly organized vascular networks in a high-throughput platform for studying developing vessel behavior in a controlled 3D environment.
10:57
Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy
0 Views •
2025
The present protocol describes a method measuring absolute DNA densities within adherent cell nuclei using Voronoi tessellation of single-molecule localization microscopy data, known volume, genome size, and cell cycle stage.
15:29
Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells
0 Views •
Cited by 6 •
2016
Two assays for microscopy-based high-throughput screening of host factors involved in Brucella infection are described. The entry assay detects host factors required for Brucella entry and the endpoint assay those required for intracellular replication. While applicable for alternative approaches, siRNA screening in HeLa cells is used to illustrate the protocols.
10:06
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
0 Views •
Cited by 8 •
2012
This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.
11:00
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
0 Views •
Cited by 4 •
2016
Stent implants in stenosed arterial curvatures are prone to "Type IV" failures involving the complete transverse fracture of stents and linear displacement of the fractured parts. We present a protocol for detection of secondary flow (vortical) structures in a curved artery model, downstream of clinically relevant "Type IV" stent failures.
FAQs
Polygon geometry converts each seed’s position into spatial measures that can be compared among neighboring cells, organisms, or localized structures. Differences in region area and shape help characterize how closely packed or spatially varied an arrangement is, while shared boundaries identify immediate relationships. This makes packing and neighborhood organization measurable rather than purely visual.
The interpretation depends on what each seed point represents. A seed may correspond to a cell in a tissue, an organism in a population, or a localized structure within a biological sample. The same geometric construction therefore supports different questions, but its biological meaning comes from the spatial entities selected and the territory each region is intended to approximate.
Adjacent polygons provide a geometry-based way to identify which biological entities occupy nearby positions. Their shared boundaries and relative placement can reveal neighborhood organization without requiring a separate categorical description of every interaction. In tissue or population studies, these relationships help compare whether spatial arrangements remain similar or become more varied across observed conditions.
Researchers can construct regions from spatial positions measured under different conditions and compare the resulting areas, shapes, and neighborhood patterns. Because the method translates arrangements into measurable regions, it provides a common framework for examining changes in cell packing, organismal territories, growth patterns, or resource distribution. The comparison focuses on spatial organization rather than position alone.
First, record the spatial positions of the relevant cells, organisms, or localized structures and designate them as seed points. Next, generate the corresponding proximity-based regions, then measure features such as region size, shape, and neighboring relationships. Finally, compare those measurements within one sample or across conditions to evaluate biological organization and spatial change.
This approach is useful when biological entities occupy distinguishable positions and their surrounding territories or neighborhoods are important to the research question. In tissues, it can support analysis of cell packing and local organization. In populations, it can help examine territories, growth patterns, and resource distribution, offering a shared framework across these spatial settings.
The method produces measurable regions that summarize how biological entities are arranged in space. These measurements can support evaluation of packing, neighborhood organization, growth patterns, and distribution of resources. Since the regions approximate rather than necessarily reproduce biological territories, their main value is as a consistent geometric representation for comparing spatial arrangements and identifying patterns.