Automated Imaging
Automated Imaging - Related Videos
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.
08:24
Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
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2016
This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.
10:49
Development of automated imaging and analysis for zebrafish chemical screens.
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Cited by 48 •
2010
We report the development of a system for automated imaging and analysis of zebrafish transgenic embryos in multiwell plates. This demonstrates the ability to measure dose dependent effects of a small molecule, BCI, on Fibroblast Growth Factor reporter gene expression and provide technology for establishing high-throughput zebrafish chemical screens.
07:06
Semi-automated Imaging of Tissue-specific Fluorescence in Zebrafish Embryos
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Cited by 6 •
2014
Described here is a protocol for semi-automated imaging of tissue-specific fluorescence in zebrafish embryos.
FAQs
Programmed acquisition keeps imaging conditions consistent across samples, helping researchers compare biological structures or events without relying on changing manual settings. Optical conditions determine how specimens are captured, while experimental conditions establish the context for each dataset. This consistency supports reproducible measurements of morphology, growth, movement, and protein localization across repeated observations.
These analysis steps convert images into structured biological information. Segmentation identifies relevant features or regions, classification assigns them to meaningful categories, and quantitative measurement records properties that can be compared among samples. Together, they allow analysis software to move beyond image collection and evaluate changes in cells, tissues, microorganisms, or model organisms.
Programmed instruments and software apply the same acquisition and analysis sequence with limited manual intervention. This reduces differences caused by inconsistent handling or interpretation during repeated measurements. The resulting standardization is especially valuable when studies examine many samples or compare biological changes over time, because measurements can be generated using a consistent workflow.
The approach can process large image datasets while maintaining defined acquisition and analysis procedures. High-throughput studies use this capacity to evaluate numerous biological samples, whereas longitudinal analysis follows changes in the same type of biological process over time. These capabilities make it possible to examine patterns in growth, movement, morphology, or protein localization at a broader scale.
A typical workflow begins with an automated microscope acquiring images under defined optical and experimental conditions. Software then processes the images, identifies relevant features through segmentation or classification, and performs quantitative measurements. The resulting data can be used to compare biological structures or events across samples, supporting consistent interpretation with limited manual intervention.
Researchers may choose it when a study requires consistent measurements across many images or repeated observations. The method is relevant to phenotypic screening, disease research, drug discovery, and studies of biological processes over time. It can examine cells, tissues, microorganisms, and model organisms, including changes in morphology, growth, movement, and protein localization.
Automated imaging can generate quantitative information about morphological features, growth, movement, and protein localization. It can also distinguish relevant image features through classification and measure them after segmentation. These outputs help researchers compare biological responses and identify changes within image datasets, rather than relying only on visual inspection of individual specimens.
In phenotypic screening, automated imaging supports the measurement of observable biological changes across image datasets. In drug discovery, the same capacity can help evaluate image-based effects in cells, tissues, microorganisms, or model organisms. Consistent acquisition and quantitative analysis make it possible to compare phenotypes across many samples and investigate responses relevant to disease research.