Image-guided Microscopy
Image-guided Microscopy - Related Videos
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.
09:26
Experimental Screening Protocols, Immunocytochemistry and Microscopy-based Imaging Techniques for Penium margaritaceum
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2025
This report describes the fundamental methods used to culture and experimentally manipulate the unicellular streptophyte alga, Penium margaritaceum. It also provides fundamental protocols of microscopy-based imaging, including live cell labeling with monoclonal antibodies and other fluorescent probes and scanning electron microscopy.
11:15
A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
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Cited by 105 •
2016
This article provides an in depth guide for the assembly and operation of a structured illumination microscope operating with total internal reflection fluorescence illumination (TIRF-SIM) to image dynamic biological processes with optical super-resolution in multiple colors.
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Cited by 14 •
2014
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
10:48
Imaging Biological Samples with Optical and Confocal Microscopy
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut Optical microscopes have been around for centuries, and while they reached their theoretical limitation of resolution decades ago, new equipment and techniques, such as confocal and digital image processing, have created new niches within the field of optical imaging. The best optical microscopes will typically have a resolution down to 200 nm in ideal...
FAQs
Spatial coordinates convert the position of a selected biological feature into a reproducible targeting reference. When combined with recorded images, they help the microscope return attention to the same location or adjust the field of view toward a chosen structure. In neuroscience, this supports consistent sampling of neurons, neural circuits, or cellular activity within complex tissue.
Recorded images provide a reference for identifying structures, whereas real-time feedback supports decisions during acquisition. Together, these inputs can guide microscope positioning, refine the observed field, and direct measurements toward the intended feature. This distinction matters when tissue contains many nearby structures, because the experiment can connect a measurement to a visually selected anatomical or functional target.
Image-guided microscopy strengthens longitudinal analysis by helping researchers follow the same structures across repeated imaging sessions. Tracking those structures allows microscopic changes to be considered alongside their prior appearance rather than treated as unrelated observations. For neuroscience, that continuity can clarify how brain organization, development, or disease-associated changes evolve in specific neurons or circuits.
A practical workflow begins by identifying a biological feature in a recorded image, associating it with spatial information, and using that reference to position the microscope or adjust its field of view. Real-time feedback then helps direct acquisition toward the intended target. The resulting observations can be linked back to the selected structure and its surrounding anatomy.
Within neuroscience, the approach can be directed toward neurons, neural circuits, and cellular activity, even when these features occur within complex tissue. The selected target determines what the microscope monitors and what measurements can be compared. This flexibility makes the method relevant to studies that need to connect cellular-scale observations with larger patterns of neural organization or function.
It can support questions about brain organization, development, and disease by improving the precision and repeatability of microscopic observations. Researchers can use guided measurements to relate visible changes in cells or circuits to anatomy or function, and to compare observations obtained from the same structures over time. The outcome is a more interpretable quantitative record of neural change.