Subcellular Region Segmentation
Subcellular Region Segmentation - Related Videos
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 8 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
11:19
Imaging Subcellular Structures in the Living Zebrafish Embryo
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Cited by 8 •
2016
Imaging the dynamic behavior of organelles and other subcellular structures in vivo can shed light on their function in physiological and disease conditions. Here, we present methods for genetically tagging two organelles, centrosomes and mitochondria, and imaging their dynamics in living zebrafish embryos using wide-field and confocal microscopy.
05:04
An Immunolabeling Technique to Visualize the Subcellular Localization of Proteins
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2025
This video demonstrates a technique for the immunolabeling of sigma-1 receptors in a murine heart tissue section using quantum dots. The heart tissue is fixed, osmicated, stained, dehydrated, embedded in resin, and then sectioned. The sections are then treated with antibodies and labeled with quantum dots to demarcate the subcellular localization of Sigma-1 receptors using electron microscopy.
03:24
Anterior Eye Segment Dissection: A Method to Isolate Cornea Bearing Anterior Segment from Porcine Eye
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2025
In this video, we demonstrate the dissection of the cornea-bearing anterior segment of the porcine eye. The isolated segment can be used for organ culturing to study the pathophysiology of eye disorders.
01:32
Subcellular Fractionation
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2023
The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation. Differential Centrifugation Differential centrifugation is...
FAQs
The analysis can combine several cues to assign pixels to biologically meaningful regions. Intensity differences distinguish areas with contrasting signal, while fluorescent labels provide region-specific visual information. Boundary detection emphasizes edges, and machine-learning models can learn image patterns. These alternatives allow the segmentation strategy to match the available microscopy data and the cellular structure under study.
Initial pixel assignments may merge adjacent structures into one connected region when their boundaries are difficult to distinguish. Postprocessing helps separate touching structures and refine their shapes after the primary segmentation step. This is important because merged or poorly outlined regions can distort measurements of size, shape, and spatial organization in microscopy-based cellular analysis.
The selected image cue, the visibility of fluorescent labels or intensity differences, and the clarity of structure boundaries all influence the resulting regions. The type of cellular compartment also matters because nuclei, cytoplasm, organelles, and membrane-associated areas may present different visual patterns. Refining shapes after initial assignment can further improve the biological usefulness of the output.
Visual inspection shows cellular features, but segmentation converts those features into delineated regions that can be analyzed quantitatively. Instead of relying only on appearance, researchers can compare region size, shape, and spatial organization across images. This distinction makes the method useful for detecting structured cellular changes associated with development, disease conditions, or experimental treatments.
A workflow begins with a microscopy image and identifies pixels using intensity differences, fluorescent labels, boundary information, or a machine-learning model. The resulting assignments are then processed to separate touching structures and refine region outlines. Researchers can subsequently extract measurements such as size, shape, and spatial organization, producing data suitable for biological comparison.
The approach can be applied to several types of subcellular regions, including nuclei, cytoplasm, organelles, and membrane-associated compartments. The relevant image signal may differ among these targets, so researchers select cues that reveal the region most clearly. This flexibility supports analyses focused on individual compartments as well as relationships among multiple regions within a cell.
Measurements derived from segmented regions can reveal how cellular size, shape, and spatial organization change across developmental stages, disease conditions, or experimental treatments. In biology, these quantitative outputs support investigations of cell function, signaling, morphology, and disease-related cellular alterations. Comparing the measurements across conditions can connect visible microscopy changes with broader experimental questions.