Serial Block Face Imaging
Serial Block Face Imaging - Related Videos
03:25
Imaging of Neuronal Mitochondria Using a Serial Block-Face Scanning Electron Microscope
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2025
Source: Mukherjee, K., et. al. Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy. J. Vis. Exp. (2016)This video demonstrates the preparation, imaging, and analysis of mouse brain tissue using a serial block-face scanning electron microscope (SBFSEM) to visualize mitochondrial ultrastructure, enabling precise mapping of their morphology and spatial distribution in axonal and dendritic neuronal compartments.
02:08
Assessing Muscle Contraction with Neuromuscular Blocker Treatment
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2025
This video demonstrates assessing the muscle using cultured motor neurons, Schwann cells, and myotubes, followed by treatment with a neuromuscular blocker to inhibit contraction. Color-coded time-motion graphics show a color-coded time-motion graphic. Red indicates the fastest movement, and blue indicates the slowest.
07:45
Quantifying Intermembrane Distances with Serial Image Dilations
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Cited by 11 •
2018
The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.
01:03
Serial Position Effect
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2025
The serial position effect is a cognitive phenomenon where individuals are more likely to recall the first and last items in a list compared to those in the middle. This effect is divided into the primacy effect and the recency effect. The primacy effect is observed when the initial items in a list are remembered better. This occurs because these items are rehearsed more frequently or receive more elaborative processing, allowing them to be encoded into long-term memory more effectively. For...
10:53
Serial Dilutions and Plating: Microbial Enumeration
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2023
Source: Jonathan F. Blaize1, Elizabeth Suter1, and Christopher P. Corbo1 1 Department of Biological Sciences, Wagner College, 1 Campus Road, Staten Island NY, 10301 Quantitative assessment of prokaryotes can be onerous given their abundance, propensity for exponential proliferation, species diversity within a population, and specific physiological needs. Compounding this challenge, is the four-phase nature in which bacteria replicate (lag, log, stationary and death). The ability to accurately...
FAQs
Its key analytical advantage comes from converting a sequence of surface observations into a spatially ordered dataset. As each newly exposed face is recorded after material removal, neighboring images represent successive positions through the specimen. Computational alignment then places these views into correspondence, allowing internal features to be followed across depth rather than interpreted as unrelated two-dimensional snapshots.
Alignment connects corresponding structures between successive images, while reconstruction organizes the aligned data into a three-dimensional representation. This processing is essential because the scientific value lies not only in individual high-resolution views, but also in the relationships among features across multiple layers. The resulting model can reveal organization and pathways that isolated images cannot show.
A two-dimensional image shows structure within one exposed plane, whereas a reconstructed volume shows how features continue, connect, and change through depth. In environmental materials, that added spatial context can help relate physical organization to transport pathways, microbial habitats, and material transformations. It is particularly useful when soil, sediment, or biofilm structures are heterogeneous and difficult to interpret from one section.
The workflow begins with a prepared specimen positioned for imaging in a scanning electron microscope. An ultramicrotome or focused cutting system removes a thin layer from the exposed block face, and the newly revealed surface is imaged. Repeating removal and acquisition produces a series of images, which are then computationally aligned and reconstructed to analyze internal architecture.
The central equipment includes a scanning electron microscope for recording the exposed surface and either an ultramicrotome or a focused cutting system for removing successive layers. Automated image acquisition maintains the sequence as new faces appear. Computational tools are then required to align the image series and generate a three-dimensional reconstruction suitable for examining environmental microstructures.
Researchers can apply the method to soil, sediments, biofilms, and other heterogeneous environmental materials when internal organization matters. Its three-dimensional datasets support examination of how microstructures create transport pathways, define microbial habitats, or accompany material transformations. The approach is most informative when high spatial resolution and volumetric context are needed to connect physical structure with environmental function.