3d Image Stack
3d Image Stack - Related Videos
09:38
Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method
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Cited by 7 •
2017
Scalable engineered blood vessels would improve clinical applicability. Using easily sizable 3D-printed guides, rings of vascular smooth muscle were created and stacked into a tubular form, forming a vascular graft. Grafts can be sized to meet the range of human coronary artery dimensions by simply changing the 3D-printed guide size.
08:00
Imaging and 3D Reconstruction of Cerebrovascular Structures in Embryonic Zebrafish
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Cited by 1 •
2014
Imaging of cerebrovascular development in larval zebrafish is described. Techniques to facilitate 3D imaging and modify cerebrovascular development using chemical treatments are also provided.
12:45
Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
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Cited by 10 •
2022
Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images.
09:39
TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis
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Cited by 3 •
2022
TrackMate Analysis of Calcium Imaging (TACI) is an open-source ImageJ plugin for 3D calcium imaging analysis that examines motion on the z-axis and identifies the maximum value of each z-stack to represent a cell's intensity at the corresponding time point. It can separate neurons overlapping in the lateral (x/y) direction but on different z-planes.
14:25
Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Cited by 7 •
2013
A technique for performing quantitative three-dimensional (3D) imaging for a range of fluid flows is presented. Using concepts from the area of Light Field Imaging, we reconstruct 3D volumes from arrays of images. Our 3D results span a broad range including velocity fields and multi-phase bubble size distributions.
FAQs
Alignment places optical sections into their correct spatial relationship before reconstruction. This step helps ensure that cells, neuronal processes, and anatomical regions remain positioned consistently across depths rather than appearing displaced between planes. In neuroscience, reliable alignment is therefore important for interpreting tissue organization and for performing later three-dimensional measurements on the reconstructed volume.
Images collected at successive depths preserve information about how structures extend through tissue, rather than restricting analysis to a single surface. This depth-dependent information helps reveal the spatial arrangement of cells, processes, and anatomical regions. It also supports analysis of neuronal morphology and tissue architecture when structural features occupy multiple optical sections.
Reconstruction combines aligned planes so that relationships among structures can be examined throughout a volume. This makes it possible to study the form and distribution of neuronal features, tissue organization, and lesions in three dimensions. The resulting spatial context supports measurements and comparisons that depend on the arrangement of structures across multiple depths.
The reconstructed volume provides a dataset in which structures can be separated through segmentation, meaning the identification of distinct features within the images. Once features are segmented, researchers can perform three-dimensional measurements of neuronal morphology, lesion distribution, or tissue architecture. These analyses convert visual information into structural data suitable for comparison across experimental conditions.
A typical workflow begins by collecting optical sections through brain tissue at successive depths. The image planes are then aligned so that corresponding structures maintain consistent positions, after which software reconstructs the aligned data into a volume. Researchers can subsequently visualize the result, segment features, make three-dimensional measurements, and compare datasets from different experimental conditions.
Researchers can apply the approach when they need to examine neuronal morphology, tissue architecture, lesion distribution, or features related to connectivity in three dimensions. It is especially useful for comparing structural organization across experimental conditions. By preserving spatial relationships throughout the volume, the dataset can help characterize brain organization and disease-related structural changes.