3d Model Generation
3d Model Generation - Related Videos
03:33
Generating a 3D Co-Culture Model of Human Corneal Fibroblasts and Neurons
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2025
This video demonstrates a technique to generate a three-dimensional co-culture model system of primary human corneal fibroblasts and differentiated neuronal cells. This co-culture system can be used to study the stromal-nerve interactions.
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.
04:43
Esophageal 3D Raft Culture Model: An In Vitro Culture Technique to Generate Human Esophageal Mucosa Model
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2023
This video describes an in vitro culture technique to generate human organotypic esophageal constructs by co-culturing primary human esophageal fibroblast and squamous epithelial cells within a decellularized porcine scaffold. These 3D culture models mimic the function and physiology of their in vivo tissue counterparts.
04:07
Generation of Organotypic Full Skin Reconstructs: A Procedure to Establish 3D Skin Model Using Human Skin Samples
0 Views •
2023
This video describes the detailed protocol for generating in vitro 3D organotypic skin reconstructs to study melanoma progression. This skin model mimics both the architecture and multicellular complexity of organs in vivo.
05:47
Generation of Human 3D Lung Tissue Cultures (3D-LTCs) for Disease Modeling
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Cited by 46 •
2019
Here, we present a protocol for the preparation of agarose-filled human precision-cut lung slices from resected patient tissue that are suitable for generating 3D lung tissue cultures to model human lung diseases in biological and biomedical studies.
FAQs
Spatial fidelity comes from retaining relationships among reconstructed structures rather than presenting isolated shapes. Image acquisition supplies the underlying observations, segmentation identifies brain regions or cellular features, and geometric reconstruction converts those elements into organized forms. Digital rendering then makes the result interactive, allowing researchers to inspect how components are positioned relative to one another.
Segmentation separates brain regions or cellular features within the acquired data so each element can be reconstructed as a distinct component. This separation supports analysis of individual shapes and their spatial organization. If researchers need to compare structures or examine relationships among them, segmentation provides the basis for meaningful geometric reconstruction and measurement.
Spatial measurements and computational data provide information that extends beyond visual appearance alone. They can encode where structures occur and how their geometry relates to surrounding features, giving reconstruction a quantitative basis. Incorporating these data helps transform observations into models that support measurable analysis of shape, organization, and relationships within neural anatomy.
A typical workflow begins with image acquisition, followed by segmentation of brain regions or cellular features. Researchers then perform geometric reconstruction and digital rendering to create an interactive representation. Keeping these stages connected helps link experimental observations to a model that can be inspected visually while also supporting quantitative investigation of neural structure.
The models provide information about shape and spatial organization, helping researchers examine how neural structures relate to one another. They can also support mapping of connectivity and analysis of changes associated with development or disease. Because the representations are interactive, investigators can combine visual interpretation with measurements of reconstructed structures.
This approach is useful when researchers need to visualize neural anatomy, map connectivity, or investigate developmental and disease-related changes. It also supports construction of virtual environments for simulation. By combining experimental data with computational methods, the resulting models can improve interpretation, communication, and quantitative investigation across different neuroscience studies.