Roi Tool Quantification
Roi Tool Quantification - Related Videos
07:28
Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections
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Cited by 4 •
2022
The present protocol describes and compares the procedure to perform a full-region or sub-region of interest analysis of sagittal mouse brain sections to quantify amyloid-beta load in the APP/PS1 transgenic mouse model of Alzheimer's disease.
10:37
Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration
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2023
The present study describes decellularization-based methodologies for visualizing and quantifying intramuscular adipose tissue (IMAT) deposition through intact muscle volume, as well as quantifying metrics of individual adipocytes that comprise IMAT.
08:02
Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images
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2024
This novel protocol entails the quantification of cardiovascular calcification progression from serial micro positron emission tomography (PET)/micro computed tomography (CT) images in small research animals.
10:26
A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
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Cited by 5 •
2015
The goal of this study is to use magnetic resonance venography with long-circulating gadolinium-based contrast agent and direct thrombus imaging for quantitative evaluation of DVT volume in a multicenter, clinical trial setting. Inter- and intra-observer variability assessments were conducted, and reproducibility of the protocol was determined.
08:44
Invertebrate Lifespan Quantification
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2023
Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an organism\'s birth and death is called its lifespan, which can be influenced by various biological and environmental factors. By exposing organisms to different growth conditions, scientists can better understand the factors affecting lifespan. Flies and worms are ideal organisms to perform such experiments, given their short generation time and...
FAQs
Boundary placement determines which pixels, cells, or anatomical features contribute to a measurement. Including surrounding tissue can dilute signal, whereas excluding relevant structures can underestimate area, volume, fluorescence, or intensity. Researchers therefore apply the same anatomical or experimental criteria across samples, making differences more likely to reflect biological variation rather than inconsistent delineation.
The measurement should match the biological feature represented in the dataset. Area or volume can describe anatomical extent, while fluorescence or intensity can characterize labeled structures or activity-related signal. Selecting one feature does not replace the others; the appropriate choice depends on whether the study asks about size, spatial distribution, signal strength, or changes across experimental conditions.
Standardized criteria reduce measurement variability introduced by different boundaries, viewing decisions, or extraction rules. Applying consistent ROI placement and numerical measurement procedures supports comparisons among samples and conditions and strengthens reproducibility. Without this consistency, an apparent group difference may arise from the quantification process rather than from altered anatomy, cellular organization, or neural activity.
A typical workflow begins with identifying the relevant structure or signal in an imaging or experimental dataset, followed by delineating the region with defined boundaries. The researcher then applies the selected measurement criteria, extracts numerical features, and organizes the values for comparison. Keeping the same workflow across datasets helps produce measurements suitable for statistical analysis.
The extracted values can reveal differences in anatomical extent, cellular organization, fluorescence, signal intensity, or activity within selected regions. These numerical outcomes allow researchers to compare samples or experimental conditions and to summarize spatial patterns quantitatively. Interpretation remains tied to the measured feature, so a change in intensity should not automatically be treated as a change in structure.
It is useful when disease-related effects appear as localized changes in brain anatomy, cellular organization, or neural signal. By applying defined regions and consistent criteria, researchers can compare affected and reference samples using numerical measurements. This approach helps connect spatially observed alterations with broader biological questions while preserving the regional detail that whole-dataset summaries may overlook.