Fluorescence Image Segmentation
Fluorescence Image Segmentation - Related Videos
06:46
Non-invasive In Vivo Fluorescence Optical Imaging of Inflammatory MMP Activity Using an Activatable Fluorescent Imaging Agent
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Cited by 4 •
2017
This paper explains the application of fluorescent imaging using an activatable optical imaging probe to visualize the in vivo activity of key matrix metalloproteinases in two different experimental models of inflammation.
16:44
Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
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Cited by 15 •
2009
We suggest a Born normalized approach for Optical Projection Tomography (BnOPT) that accounts for the absorption properties of imaged samples to obtain accurate and quantitative fluorescence tomographic reconstructions. We use the proposed algorithm to reconstruct the fluorescence molecular probe distribution within small animal organs.
06:27
Automated Analysis of C. elegans Fluorescence Images using SegElegans
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2025
Here we provide instructions on effectively utilizing SegElegans, a deep learning system we developed for the automated segmentation of individual worms in widefield microscopy images, for subsequent use in image analysis software such as ImageJ. We provide ways to use the system both online and offline.
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.
10:41
Generation of Dispersed Presomitic Mesoderm Cell Cultures for Imaging of the Zebrafish Segmentation Clock in Single Cells
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Cited by 8 •
2014
Somitogenesis is a rhythmic developmental process that spatially patterns the body axis of vertebrate embryos. Previously, we developed transgenic zebrafish lines that use fluorescent reporters to observe the cyclic genes that drive this process. Here, we culture dispersed cells from these lines and image their oscillations over time in vitro.
FAQs
Thresholding separates pixels according to fluorescence intensity or color, while edge detection focuses on changes that indicate boundaries between regions. Machine learning uses patterns in the image to assign pixels to defined regions. These approaches provide different ways to construct masks, allowing researchers to select a strategy suited to the visual characteristics of their microscopy data.
Intensity can distinguish fluorescent structures from surrounding background, whereas color can help separate signals associated with different labels or regions. Spatial patterns add information about how pixels are arranged within an image. Combining these features supports more meaningful region assignments than relying on a single visual property alone.
A mask records which pixels belong to each defined biological region, creating a basis for downstream measurement. Researchers can use these regions to determine object number, size, shape, and fluorescence intensity, rather than relying only on visual inspection. The same masks can also support analysis of spatial relationships among cells, organelles, or other structures.
A typical workflow begins with fluorescence microscopy data and analyzes pixel intensity, color, or spatial patterns to distinguish biological structures from background. An approach such as thresholding, edge detection, or machine learning then assigns pixels to regions and produces masks. Researchers can use those masks to calculate measurements and compare image-derived features across biological samples.
Segmentation isolates cellular regions so their dimensions and outlines can be measured quantitatively. From the resulting masks, researchers can examine cell number, size, shape, and fluorescence intensity, which provides a structured way to study morphology. This converts visual differences among cells into measurements that can be compared across microscopy images and biological conditions.
For protein localization, segmented fluorescence regions can help quantify where labeled signals occur within cells or other biological structures. In tissue studies, the same approach supports analysis of how labeled cells or structures are arranged relative to one another. These measurements help investigate organization in biological systems and changes associated with development or disease.