Automated Measurements
Automated Measurements - Related Videos
11:39
Measuring the Confluence of iPSCs Using an Automated Imaging System
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2020
The goal of the protocol is to compare different extracellular matrix (ECM) coating conditions to assess how differential coating affects the growth rate of induced pluripotent stem cells (iPSCs). In particular, we aim to set up conditions to obtain optimal growth of iPSC cultures.
08:14
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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Cited by 93 •
2013
Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
14:18
Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
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Cited by 2 •
2016
A protocol to detect and automate mode locking in a pre-adjusted nonlinear polarization rotation fiber laser is presented. The detection of a sudden change in the output polarization state when mode locking occurs is used to command the alignment of an intra-cavity polarization controller in order to find mode-locking conditions.
10:56
Automated Measurement of Microcirculatory Blood Flow Velocity in Pulmonary Metastases of Rats
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Cited by 2 •
2014
A method is presented to measure microcirculatory blood flow velocity in pulmonary cancer metastases of the pleural surface in rats in an automated fashion, using closed-chest pulmonary intravital microscopy. This model has potential to be used as a widespread tool to perform physiologic research on pulmonary metastases in rodents.
FAQs
The process links signal acquisition with algorithmic feature analysis. An imaging or sensing system first records information from a biological sample, then predefined software detects features or classifies objects and calculates values such as cell number, fluorescence intensity, size, or growth rate. This division between acquisition and analysis supports consistent comparisons across samples and time points.
Predefined algorithms reduce the role of individual judgment when software detects features, classifies objects, and calculates measurements. Because the same computational rules can be applied across samples, researchers can limit observer-dependent variation and obtain more consistent comparisons. This is especially useful when experiments contain many samples or when differences between conditions are subtle.
Standardized measurement conditions make values more comparable across samples and time points. Consistency in how signals are acquired and analyzed helps distinguish biological changes from variation introduced by observation. As a result, automated measurements can reveal gradual or subtle differences in cell number, fluorescence intensity, size, or growth rate across biological conditions.
Key components are a biological sample, an imaging or sensing system, and software containing predefined analytical rules. The instrument supplies the signal, while the algorithms detect features, classify objects, and calculate measurements. Together, these components support quantitative observations with limited manual intervention and allow the same workflow to operate across many samples.
Automated measurements are useful in cell biology, microbiology, developmental biology, and drug screening. In each setting, they can quantify features such as cell number, size, fluorescence intensity, or growth rate rather than relying only on descriptive observation. Their ability to process many samples also supports experiments designed around larger biological comparisons.
In drug screening, automated measurements enable researchers to compare quantitative biological responses across many samples, helping identify changes associated with experimental conditions. Measurements of cell number, fluorescence intensity, size, or growth rate provide different ways to characterize those responses. Consistent acquisition and analysis can improve reproducibility and expose subtle effects across screening experiments.