Automated Microscopy Imaging
Automated Microscopy Imaging - Related Videos
09:30
Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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Cited by 14 •
2017
We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using Förster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in µManager and data analysis is undertaken in FLIMfit.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
07:31
Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy
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Cited by 157 •
2011
This protocol provides a step-by-step procedure to monitor single cell behavior of different bacteria in time using automated fluorescence time-lapse microscopy. Furthermore, we provide guidelines how to analyze the microscopy images.
06:27
Automated Analysis of C. elegans Fluorescence Images using SegElegans
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2025
Here we provide instructions on effectively utilizing SegElegans, a deep learning system we developed for the automated segmentation of individual worms in widefield microscopy images, for subsequent use in image analysis software such as ImageJ. We provide ways to use the system both online and offline.
06:49
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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Cited by 28 •
2014
Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.
FAQs
The system links motorized stage movement, automated focusing, programmed illumination, image capture, and computational analysis into a coordinated sequence. The stage positions the sample repeatedly, focusing keeps acquired fields usable, and controlled illumination supports consistent imaging across locations or experimental conditions. The resulting workflow can organize measurements from many images rather than treating each field as an isolated observation.
These components address different requirements in repeated biological measurements. Motorized stages support imaging at multiple positions, automated focusing helps maintain appropriate image acquisition, and programmed illumination coordinates how samples are exposed during capture. Together, they improve consistency across an imaging run, which is important when researchers compare cell morphology, localization, growth, or behavior between conditions.
Automated microscopy imaging reduces the need for continuous manual control during image acquisition. Instead of relying on an operator to reposition, refocus, and capture each field, programmed components coordinate repeated measurements. This can increase throughput and consistency, while also producing more organized datasets for quantitative analysis. Manual intervention may still be limited rather than completely absent.
Image-analysis algorithms convert captured biological images into quantitative measurements of features such as cell morphology, localization, growth, and behavior. These measurements allow researchers to examine complex samples systematically instead of relying only on visual inspection. When applied across many positions, time points, or experimental conditions, the analysis can reveal patterns suitable for comparison and reproducible study.
A workflow generally programs the imaging conditions, positions the motorized stage, applies automated focusing and illumination, and captures images at selected locations or time points. Image-analysis algorithms then process the acquired data, while organizational tools arrange results across experimental conditions. This sequence supports consistent collection and measurement of biological images with limited manual intervention.
Biologists use this approach for high-content screening, live-cell imaging, and quantitative studies of cellular processes. It can support treatment evaluation by comparing cellular responses across experimental conditions, while time-resolved acquisition can track growth or behavior. The resulting datasets help researchers measure complex samples at higher throughput and generate more reproducible evidence about biological changes.