3d Quantification
3d Quantification - Related Videos
08:08
Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model
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Cited by 16 •
2014
This article provides detailed methodologies for the use of three-dimensional (3D) assays to quantify breast cancer cell invasion. Specifically, we discuss the procedures required to set up such assays, quantification, and data analysis, as well as methods to examine the loss of membrane integrity that occurs when cells invade.
12:45
Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
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Cited by 10 •
2022
Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images.
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...
05:10
Three-dimensional Quantification of Intestinal Mucus Using Whole-mount Tissue Imaging
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2025
This study presents a 3D imaging method using whole-mount intestinal tissues and multi-photon microscopy to quantify secreted mucus, enabling precise volumetric analysis and visualization of mucus dynamics in response to stimuli like carbamoylcholine chloride.
05:25
Quantification of Dendritic Spines Using Confocal Microscopy Imaging
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2025
Source: Gouder, et. al. Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells. J. Vis. Exp. (2015).This video demonstrates the imaging and analysis of dendritic spines in transduced, immunolabeled pyramidal neurons using confocal microscopy. Background noise is reduced, and dendrites are traced by estimating their diameter and length. Automated spine segmentation involves setting parameters for dendrite and spine dimensions...
FAQs
Segmentation isolates the anatomical structure, lesion, or other feature that will be measured from the surrounding medical image data. The resulting region of interest determines which voxels contribute to calculations such as volume, surface area, and shape. Consequently, segmentation quality directly influences the objectivity and consistency of measurements used for patient evaluation or research.
Visual inspection can identify patterns and abnormalities, but it does not by itself provide standardized numerical measurements. 3D quantification adds measurable information about size, shape, surface characteristics, and spatial relationships. These data support more objective comparisons among patients, examinations, or time points, helping clinicians and researchers evaluate changes with greater consistency.
Each measurement describes a different aspect of the finding. Volume indicates its three-dimensional extent, while shape and surface area characterize its form and boundaries. Spatial relationships show how the structure is positioned relative to other anatomy. Together, these properties provide a more complete description than a single size measurement and support patient-specific assessment.
Repeated measurements can be compared across examinations to assess disease progression or therapeutic response. Changes in calculated volume, shape, surface area, or anatomical relationships may provide quantitative evidence that a structure has changed. This longitudinal perspective complements image review and can help organize treatment monitoring around measurable differences rather than visual impressions alone.
The workflow begins with an imaging scan and conversion of the data into a three-dimensional representation. Image-processing methods then identify and segment the region of interest. After segmentation, calculations generate properties such as volume, shape, surface area, and spatial relationships. The resulting measurements can be interpreted for assessment, planning, or comparison across time.
Applications include tumor assessment, organ analysis, vascular analysis, treatment planning, and evaluation of disease progression or therapeutic response. The appropriate measurement depends on the clinical question, such as estimating lesion extent, characterizing anatomy, or tracking change. By supporting objective, patient-specific evaluation, the approach also strengthens quantitative medical-imaging research.
Three-dimensional measurements can describe the extent and position of relevant anatomy or disease features in a patient-specific way. Clinicians can use that quantitative information alongside image interpretation when evaluating treatment options or planning interventions. The added measurements help translate complex imaging data into structured information that supports planning and comparison of clinical findings.