Automated Particle Detection
Automated Particle Detection - Related Videos
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.
07:01
Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
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Cited by 5 •
2016
A scanner for imaging magnetic particles in planar samples was developed using the planar frequency mixing magnetic detection technique. The magnetic intermodulation product response from the nonlinear nonhysteretic magnetization of the particles is recorded upon a two-frequency excitation. It can be used to take 2D images of thin biological samples.
11:54
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Cited by 8 •
2017
We demonstrate a microfluidic platform with an integrated surface electrode network that combines resistive pulse sensing (RPS) with code division multiple access (CDMA), to multiplex detection and sizing of particles in multiple microfluidic channels.
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.
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.
FAQs
Thresholds determine which pixels become candidate regions for measurement. A threshold that is too restrictive may exclude dim or weakly labeled structures, whereas a less restrictive setting may admit unwanted signal as detections. Because fluorescent puncta and other neuronal features can differ in signal strength, threshold selection directly affects the number and measured properties of objects available for comparison.
Adjacent particles can appear as one connected region when their signals overlap. Separation steps allow the analysis to treat neighboring structures as individual objects rather than a single larger detection. This distinction matters in neuroscience images containing clustered puncta or closely positioned cellular structures, because merging can alter both particle counts and measurements of size or shape.
After candidate regions are identified, the software can retain or exclude detections according to size, shape, and signal quality. These criteria help distinguish relevant structures from regions that do not match the intended analysis target. Applying feature-based filters is especially useful when images contain varied signal, because it focuses measurements on particles with the characteristics required by the experiment.
Using computational rules to identify and measure objects reduces the subjectivity and labor associated with manual counting. Applying the same thresholding, separation, and filtering logic across images can produce consistent measurements for comparison. In neuroscience experiments, this supports quantitative assessment of differences in neuronal organization, molecular aggregates, or other image-based features across experimental conditions.
A typical workflow begins by converting the image into candidate regions through intensity or color thresholding. The analysis then separates neighboring objects and filters the resulting detections using properties such as size, shape, and signal quality. The retained objects can subsequently be quantified across images, providing measurements suited to comparisons of neuronal or molecular structures.
The method is useful when experiments require measurements from many microscopy images rather than isolated manual counts. Neuroscience applications described for it include quantifying fluorescent puncta, cellular structures, and molecular aggregates. These measurements can help compare neuronal organization or disease-associated changes and can evaluate how experimental treatments affect image-based features.
The analysis can provide quantitative measurements of discrete image objects after candidate regions have been identified, separated, and filtered. Depending on the target and selected criteria, researchers can assess features such as particle number, size, shape, and signal quality. These outcomes support reproducible comparisons across images and help characterize changes in neuronal or molecular organization.