Three Dimensional Image Analysis
Three Dimensional Image Analysis - Related Videos
09:47
FRET Imaging in Three-dimensional Hydrogels
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Cited by 3 •
2016
Förster resonance energy transfer (FRET) imaging is a powerful tool for real-time cell biology studies. Here a method for FRET imaging cells in physiologic three-dimensional (3D) hydrogel microenvironments using conventional epifluorescence microscopy is presented. An analysis for ratiometric FRET probes that yields linear ratios over the activation range is described.
10:31
Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers
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2017
This protocol uses three-dimensional (3D) imaging and analysis techniques to visualize and quantify nerve-specific mitochondria. The techniques are applicable to other situations where one fluorescent signal is used to isolate a subset of data from another fluorescent signal.
01:27
Dimensional Analysis
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2025
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena. In fluid mechanics, dimensional...
02:19
Dimensional Analysis
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2023
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
09:55
Three-Dimensional Imaging of Aortic Tissues in Atherosclerosis
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2024
Here, we present optimized tissue-clearing protocols to image the murine aorta in three dimensions (3D). We delineate state-of-the-art procedures for immunostaining, optical clearing, and imaging with the intent to define the anatomical proximity of the peripheral nervous system with atherosclerotic plaques and the adventitia in atherosclerosis.
FAQs
Alignment places images from different sections or acquisition planes into a consistent spatial arrangement before structures are measured. Without this step, apparent changes in position, shape, or connectivity may reflect mismatched image locations rather than biology. In neuroscience, reliable alignment is especially important when reconstructing brain regions, neural cells, or tissue organization across a volumetric dataset.
Segmentation separates regions or objects of interest from the surrounding image volume, creating the basis for quantitative analysis. The resulting boundaries determine which pixels or voxels contribute to measurements such as volume and shape. For neural tissue or individual cells, appropriate segmentation therefore supports more meaningful comparisons of anatomy, organization, and treatment-related structural changes.
Beyond visualizing a structure, the analysis can quantify its volume, shape, and position, along with spatial relationships between structures. These measurements convert a complex image volume into data that can be compared across samples or experimental conditions. In neuroscience, that information can describe brain anatomy, neural-cell organization, tissue structure, or connections.
Visual inspection can reveal overall patterns, but it does not by itself provide standardized measurements of volume, shape, position, or spatial relationships. Three dimensional image analysis adds computational reconstruction and quantification, reducing reliance on subjective judgment. This makes it useful when research requires structural comparisons linked to development, neurodegeneration, circuit organization, or treatment-related change.
A typical workflow begins by aligning the image stack, then segmenting relevant regions or objects, measuring their three-dimensional properties, and generating a three-dimensional rendering when visualization is useful. The sequence moves from organizing the volumetric data to defining analyzable structures and finally to interpreting their geometry and spatial relationships.
Researchers can apply it to volumetric datasets containing brain anatomy, neural cells, tissue organization, or connections. The resulting structural measurements support studies of development, neurodegeneration, circuit organization, and treatment-related changes. Its value is greatest when complex three-dimensional patterns must be reconstructed and compared quantitatively rather than described only through visual impressions.