Region Of Interest Analysis
Region Of Interest Analysis - Related Videos
08:51
Cerebellar Regional Dissection for Molecular Analysis
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Cited by 4 •
2020
Different cerebellar regions have been implicated to play a role in distinct behavioral outputs, yet the underlying molecular mechanisms remain unknown. This work describes a method to reproducibly and quickly dissect cerebellar cortex of the hemispheres, anterior and posterior regions of the vermis, and the deep cerebellar nuclei in order to probe for molecular differences by isolating RNA and testing for differences in gene...
05:58
Preparation of Single-cell Suspensions for Cytofluorimetric Analysis from Different Mouse Skin Regions
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Cited by 21 •
2016
The skin is home to a complex immune cell network. We describe an efficient methodology for the digestion of mouse skin, from different parts of the animal's body, in order to obtain a single-cell suspension and analyze the different leukocyte populations resident in the skin by flow cytometry.
01:03
IR Frequency Region: Fingerprint Region
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2024
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1. The...
04:23
Real-Time Analysis of Resident T-cell Migration in Different Tumor Regions Using Confocal Microscopy
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2025
This video describes the imaging of resident tumor-infiltrating CD8 T cells labeled with fluorescently coupled antibodies within human lung tumor slices. This technique permits real-time analyses of CD8 T cell migration using confocal microscopy.
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 10 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
FAQs
Selection criteria determine which pixels belong to the measured feature, so the numerical result depends on more than the software's measurement step. A consistently outlined cell, organelle, lesion, or tissue area allows area, shape, intensity, or fluorescence values to be compared across images. Background correction further helps distinguish feature-associated signal from surrounding image signal.
Pixel intensity and fluorescence measurements provide a signal-based view, whereas area and shape describe spatial structure. Using both types of measurements can connect a feature's physical extent with the amount or distribution of image signal assigned to it. This distinction is useful when biological interpretation depends on whether samples differ in morphology, signal level, or both.
Region Of Interest Analysis separates a selected feature from the surrounding field without requiring the entire image to be treated as one measurement. That separation matters in heterogeneous biological samples, where cells, tissues, organelles, or lesions may occupy only part of the image. The resulting measurements can convert localized visual differences into data for comparison.
A practical workflow begins by identifying the biological feature to measure, then drawing or generating a region around it. The selected pixels are analyzed for area, shape, pixel intensity, or fluorescence, and background correction is applied when appropriate. Repeating the same selection criteria across samples supports reproducible comparisons rather than relying only on visual inspection.
Reliable comparisons depend on using the same basis for selecting regions in each image. If one sample is outlined more broadly or narrowly than another, measurements may reflect selection differences rather than biology. Consistent criteria, together with background correction, make changes in measured structure or signal easier to attribute to differences among samples.
In cell morphology studies, measurements of area and shape can document structural differences between samples. In localization studies, intensity or fluorescence values within selected regions can help examine where a protein-associated signal appears. The same approach also supports analysis of tissue organization and disease-associated changes, giving biological observations a quantitative form.