Automated Detection Tracking
Automated Detection Tracking - Related Videos
14:03
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
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Cited by 38 •
2013
The use of a 3D automatic video system that can track individual and groups of zebrafish is described. As application example we explore the effects of the NMDA-receptor antagonist MK-801 on shoals of zebrafish.
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.
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.
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.
FAQs
Automated detection tracking depends on two linked operations: first, an image-analysis algorithm identifies a biological object or labeled structure in each image or video frame; second, it associates those detections across sequential frames. The second operation turns separate observations into a trajectory, allowing movement to be analyzed over time rather than as isolated positions.
Sequential frames provide the temporal structure needed to distinguish a momentary position from a movement pattern. Once detections are connected, the resulting trajectory can show where an object moved, how quickly it moved, and in which direction. This makes it possible to examine dynamic behavior rather than relying only on endpoint measurements.
The features detected may include cells, organisms, or labeled structures. Tracking these biologically meaningful features supports measurements of position, speed, direction, and changes in behavior. The same framework can therefore describe physical displacement as well as broader changes observed across a biological time series.
Automation changes the scale and consistency of observation. Instead of requiring manual observation of every frame, computational analysis can process large datasets with a consistent approach. In biological experiments, this can improve throughput and reproducibility, making comparisons across observations more practical while reducing dependence on repeated human inspection.
A typical analysis begins with an image sequence or video, followed by computational detection of the biological objects or labeled structures present in successive frames. The system then links corresponding detections, reconstructs trajectories, and extracts measurements such as position, speed, or direction. These outputs provide a quantitative record of the process being studied.
Biologists can apply the approach to questions involving cell migration, organismal movement, growth, interactions, and population dynamics. The relevant outcome depends on the biological system: trajectories can describe movement, repeated observations can reveal growth or behavioral change, and multiple tracked objects can support analysis of interactions or population-level patterns.