Cell Detection Automation
Cell Detection Automation - Related Videos
13:28
Automated Detection and Analysis of Exocytosis
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Cited by 14 •
2021
We developed automated computer vision software to detect exocytic events marked by pH-sensitive fluorescent probes. Here, we demonstrate the use of a graphical user interface and RStudio to detect fusion events, analyze and display spatiotemporal parameters of fusion, and classify events into distinct fusion modes.
08:58
Fully Automated Centrifugal Microfluidic Device for Ultrasensitive Protein Detection from Whole Blood
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Cited by 8 •
2016
This protocol demonstrates how to achieve femto molar detection sensitivity of proteins in 10 µL of whole blood within 30 min. This can be achieved by using electrospun nanofibrous mats integrated in a lab-on-a-disc, which offers high surface area as well as effective mixing and washing for enhanced signal-to-noise ratio.
07:21
Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
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Cited by 2 •
2021
Detection of host-bacterial pathogen interactions based on phenotypic adherence using high-throughput fluorescence labeling imaging along with automated statistical analysis methods enables rapid evaluation of potential bacterial interactions with host cells.
06:45
Automated Joint Space Detection Improves Bone Segmentation Accuracy
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2025
The development of an automated joint space detection workflow enabled high-throughput segmentation of distinct murine hindpaw bones with >98% accuracy in wild-type animals. Flexible application to forepaws and paws with inflammatory-erosive arthritis was achieved, but with deprecated performance that warrants further optimization in future studies using publicly available data.
06:34
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
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2025
Here, we present a semi-automated protocol for identifying and quantifying immune and non-immune cells in skin sections using SCAnED, a free ImageJ-based macro for skin segmentation.
FAQs
Detection identifies where candidate cells are located, segmentation separates cellular regions from surrounding pixels, and classification applies rules to distinguish relevant cell types or categories. These functions can be used together or separately, depending on whether the study needs cell counts, boundaries, identities, or quantitative descriptions of individual cells in microscopy images.
The distinction determines which image regions the system treats as cells rather than background. Clear, consistent separation supports more reliable localization and counting, while inconsistent rules can alter the measured number or properties of cells. In neuroscience images, this distinction is especially relevant when analyzing fluorescently labeled brain tissue containing neurons or glial cells.
Automated processing applies the same detection, segmentation, or classification rules across digital image sets. Manual analysis can require substantial time and may introduce subjectivity when different people interpret images. By standardizing how cellular features are evaluated, automation supports more reproducible measurements and makes comparisons across samples or experimental conditions easier.
The analysis can support measurements of cell distribution and morphology in addition to cell counts. Distribution describes how cells are arranged across the examined tissue, whereas morphology concerns their observable form. These measurements allow researchers to examine whether cellular organization or appearance changes across development, disease, injury, or experimental treatment.
The approach can be applied to microscopy images containing neurons, glial cells, and fluorescently labeled brain tissue. It is useful when researchers need to examine large image sets rather than isolated fields. Depending on the study, the resulting measurements can describe cell abundance, spatial distribution, or morphology within brain tissue.
Researchers may use it to compare cellular patterns associated with development, disease, injury, or an experimental treatment. Applying standardized measurements across these conditions helps reveal differences in cell number, distribution, or morphology. The computational approach is particularly valuable when image collections are large enough that manual examination would be slow or difficult to apply consistently.