Z-stack Image Analysis
Z-stack Image Analysis - Related Videos
09:38
Scaling of Engineered Vascular Grafts Using 3D Printed Guides and the Ring Stacking Method
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Cited by 6 •
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.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
07:19
Clock Scan Protocol for Image Analysis: ImageJ Plugins
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Cited by 16 •
2017
This paper describes two novel ImageJ plugins for 'Clock Scan' image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
FAQs
Analyzing signal across successive focal planes adds depth information that a single two-dimensional view cannot provide. Structures appearing superimposed in one image can be evaluated according to where they occur within the specimen, helping distinguish true spatial relationships from apparent overlap. This is especially useful when assessing whether immune cells, pathogens, or fluorescent markers occupy the same three-dimensional region.
Alignment is important because measurements become unreliable if corresponding features shift between optical sections. By bringing the planes into consistent spatial registration, software supports coherent visualization and comparison throughout the stack. This makes reconstructed structures easier to interpret and helps ensure that measurements of signal distribution, morphology, or spatial relationships reflect the specimen rather than inconsistencies among sections.
Colocalization assessment benefits from examining fluorescent signals across depth rather than judging overlap in one plane. Signals that appear coincident in two dimensions may occupy different positions within the stack, while signals that remain spatially associated through multiple sections provide stronger evidence of shared three-dimensional localization. This distinction helps interpret intracellular distribution and pathogen-marker relationships more accurately.
The workflow begins with a series of optical sections collected at different specimen depths. Analysis software then aligns the sections, reconstructs or visualizes the three-dimensional signal, and applies measurements to features of interest. Researchers can use this sequence to examine morphology, signal distribution, localization, and spatial relationships while retaining the depth information present in the original stack.
Measurements can describe cell morphology, fluorescent signal distribution, pathogen localization, intracellular marker placement, and spatial relationships among structures. Examining these features through the stack provides information that may be obscured by overlap in a single image. The resulting quantitative observations can support comparisons of tissue organization, immune-cell interactions, and host-pathogen organization within the specimen.
In immunology and infection studies, the analysis can examine how immune cells interact, where pathogens localize, how tissues are organized, and where fluorescent markers occur inside cells. These observations help researchers evaluate host-pathogen interactions and immune responses in three dimensions. Quantitative spatial information can strengthen conclusions by distinguishing apparent proximity from relationships supported across the image stack.