Three-dimensional Reconstruction
Three-dimensional Reconstruction - Related Videos
08:18
Three-Dimensional Reconstruction of Orbital Fractures
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2025
Personalized medicine for orbital reconstruction is developing rapidly. Due to the delicate nature of the orbit, small discrepancies following fracture reconstruction may cause impairment in visual perception. Here, we describe three methods for virtual 3D reconstruction of orbital defects and their indications and potential pitfalls for correct reconstruction.
10:59
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
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Cited by 10 •
2014
We present an image registration approach for 3-dimensional (3D) histology volume reconstruction, which facilitates the study of the changes of an organ at the level of macrostructures made up of cells . Using this approach, we studied the 3D changes between wild-type and Igfbp7-null mammary glands.
12:38
Three-dimensional Reconstruction of the Vascular Architecture of the Passive CLARITY-cleared Mouse Ovary
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Cited by 12 •
2017
Here we present an adaptation of the passive CLARITY and 3D reconstruction method for visualization of the ovarian vasculature and follicular capillaries in intact mouse ovaries.
07:53
Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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Cited by 2 •
2023
This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.
08:49
Cultivating a Three-dimensional Reconstructed Human Epidermis at a Large Scale
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Cited by 9 •
2021
This protocol describes a straightforward method to cultivate three-dimensional reconstituted human epidermis in a reproducible and robust manner. Additionally, it characterizes the structure-function relationship of the epidermal barrier model. The biological responses of the reconstituted human epidermis upon proinflammatory stimuli are also presented.
FAQs
Slice alignment determines whether information from separate two-dimensional images forms a coherent anatomical model. Computational algorithms position the slices relative to one another before combining them, preserving spatial relationships across the dataset. Accurate alignment supports more reliable viewing, measurement, and rendering, whereas misaligned information can make anatomical structures appear spatially inconsistent and complicate clinical interpretation.
Segmentation identifies tissue boundaries within the image data, separating relevant anatomical structures from surrounding information. This step gives the reconstruction a defined basis for representing specific tissues or regions rather than treating the entire dataset as a single undifferentiated volume. The resulting boundaries support clearer visualization, anatomical assessment, and quantitative analysis of disease-related changes.
Changing the viewing angle exposes spatial relationships that may be difficult to interpret in individual image slices. A rendered model can provide broader context for complex anatomy and help clinicians assess the location and form of structures or disease-related changes. This perspective supports communication and contributes to procedure planning by making three-dimensional relationships easier to examine.
A typical workflow begins with two-dimensional images or other spatial data, followed by computational alignment of image slices. Algorithms then identify tissue boundaries through segmentation and combine the processed information into a volumetric model. The model can subsequently be viewed, measured, or rendered from different angles, allowing users to examine anatomy and extract information for clinical or research purposes.
Procedure planning benefits from a model that presents complex anatomy with greater spatial context than separate two-dimensional views alone. Clinicians can examine relationships among anatomical structures and disease-related changes before an intervention. This visualization supports more informed planning and also contributes to image-guided intervention, where reconstructed information can help communicate and interpret anatomy during a procedure.
Beyond assessing anatomy and disease-related changes, reconstructed models support medical education, surgical simulation, and image-guided intervention. They provide a spatial representation that can be viewed and communicated in ways suited to teaching or procedural preparation. In these settings, the models help learners and clinical teams examine anatomy, rehearse spatial understanding, and discuss complex findings.
The reconstructed volume provides a model that can be measured as well as visualized. Researchers can use that measurable representation to analyze anatomical features or disease-related changes rather than relying only on visual inspection of individual images. This quantitative capability extends the value of reconstruction from communication and interpretation to structured investigation of spatial patterns in medical imaging data.