3d Shape Modeling
3d Shape Modeling - Related Videos
09:17
3D Printing of Biomolecular Models for Research and Pedagogy
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Cited by 29 •
2017
Physical models of biomolecules can facilitate an understanding of their structure-function for the researcher, aid in communication between researchers, and serve as an educational tool in pedagogical endeavors. Here, we provide detailed guidance for the 3D printing of accurate models of biomolecules using fused filament fabrication desktop 3D printers.
09:03
Morphometric Analyses of Shape: The Analysis Software Toolbox for Craniofacial Shape Quantification in Zebrafish
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2026
This work describes a protocol for quantifying craniofacial cartilage shape using free software (tpsDigs2, MorphoJ, and PAST) to measure changes in facial structure in zebrafish larvae.
03:37
Molecular Shape and Polarity
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2020
Dipole Moment of a Molecule Polar covalent bonds connect two atoms with differing electronegativities, leaving one atom with a partial positive charge (δ+) and the other atom with a partial negative charge (δ–), as the electrons are pulled toward the more electronegative atom. This separation of charge gives rise to a bond dipole moment. The magnitude of a bond dipole moment is represented by the Greek letter mu (µ) and is given by the formula shown here, where Q is the magnitude of the...
11:11
Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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Cited by 7 •
2016
Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches.
02:52
VSEPR Theory and the Basic Shapes
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2020
Overview of VSEPR Theory Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure, including approximate bond angles around a central atom, of a molecule from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them.
FAQs
Segmentation separates imaging data into anatomically meaningful regions before a model is constructed. This step determines which structures, boundaries, and spatial relationships become available for later analysis. Because the resulting surfaces or volumes depend on the regions selected, segmentation directly affects measurements of morphology, comparisons between models, and interpretation of structural differences in neuroscience.
Surface representations emphasize the geometry and boundaries of anatomical structures, whereas volumetric representations preserve information about their three-dimensional extent. Choosing between them depends on the feature being examined and the intended analysis. Surfaces can support visualization and shape comparisons, while volumes can help characterize spatial structure and quantitative morphological properties.
Meshes provide a manipulable mathematical representation of extracted anatomical surfaces. They allow complex brain forms to be visualized, measured, and compared within computational workflows. By converting imaging-derived geometry into a structured model, meshes help researchers examine morphology, represent spatial relationships, and prepare anatomical forms for visualization or simulation-related analyses.
Comparisons can focus on differences in morphology, spatial organization, neural circuit structure, developmental stage, or disease-related anatomy. The models provide a common computational form in which these features can be visualized and measured. Such comparisons help connect structural variation with the scientific question, whether it concerns normal development, brain organization, or pathological change.
A typical workflow begins with imaging data and identifies relevant anatomical regions through segmentation. Researchers then extract surfaces or volumetric features from those regions and convert the results into meshes or other mathematical representations. The completed models can subsequently be manipulated for visualization, quantitative morphometry, comparison, or interpretation of neuroimaging findings.
Researchers use it when brain structure must be examined beyond a two-dimensional image view. The models support visualization of anatomy, quantitative morphometry, analysis of neural circuits, and assessment of developmental or disease-related structural differences. They can also provide a foundation for simulation and make complex neuroimaging results easier to interpret in relation to spatial organization.