3d Quantitative Analysis
3d Quantitative Analysis - Related Videos
09:59
Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy
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Cited by 3 •
2013
Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.
11:39
A Quantitative Fitness Analysis Workflow
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Cited by 25 •
2012
Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.
01:12
Quantitative Analysis
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2024
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase. In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
08:11
Quantitative Analysis of Chromatin Proteomes in Disease
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Cited by 4 •
2012
Advances in mass spectrometry have allowed the high throughput analysis of protein expression and modification in a host of tissues. Combined with subcellular fractionation and disease models, quantitative mass spectrometry and bioinformatics can reveal new properties in biological systems. The method described herein analyzes chromatin-associated proteins in the setting of heart disease and is readily applicable to other in vivo models of human disease.
11:29
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
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Cited by 15 •
2014
A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.
FAQs
Segmentation separates tissues, organs, or lesions within a reconstructed volumetric dataset so that measurements can be assigned to specific anatomical regions. The selected boundaries determine which voxels contribute to calculations such as volume, density, and surface geometry. Consequently, segmentation provides the structural basis for comparing abnormalities, organs, or anatomical features across patients and imaging time points.
Volume, density, distance, and surface geometry provide complementary descriptions of a three-dimensional feature. Volume quantifies its spatial extent, density characterizes image-based composition, distance describes relationships between structures, and surface geometry captures shape-related properties. Together, these variables convert complex medical images into measurable features that can support tumor characterization, organ assessment, and anatomical mapping.
Measurements of spatial relationships show how structures are positioned relative to one another, while surface geometry describes the form of their boundaries. These features add information beyond overall size and can clarify anatomical organization or lesion shape within a volume. Their inclusion supports more detailed anatomical mapping and supplies quantitative variables for treatment planning and computational modeling.
A typical workflow begins by reconstructing medical images as a volumetric dataset, followed by segmentation to distinguish the tissues or lesions of interest. Analysts then calculate voxel-based variables such as volume, density, distance, or surface geometry and interpret the resulting measurements. This sequence transforms image data into objective quantities suitable for comparison, modeling, diagnosis, research, or planning.
Its medical applications include tumor characterization, organ assessment, anatomical mapping, and treatment planning. The approach is especially useful when a clinical or research question depends on the size, shape, density, or position of a structure within a volume. By expressing these features as measurable variables, it can support quantitative comparisons rather than relying only on visual interpretation.
Measurements derived from volumetric images can be compared between patients or across time points, allowing changes in anatomy or lesions to be evaluated quantitatively. This longitudinal perspective supports monitoring, while computational modeling can help interpret spatial features and inform treatment planning. Together, these capabilities may strengthen research, diagnosis, and personalized care by linking image-derived measurements to individual anatomy.