3d Slicer
3d Slicer - Related Videos
15:53
Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning
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Cited by 33 •
2016
The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.
07:58
Invasion Assay Using 3D Matrices
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2023
The extracellular matrix (ECM) is a network of molecules that provide a structural framework for cells and tissues and helps facilitate intercellular communication. Three-dimensional cell culture techniques have been developed to more accurately model this extracellular environment for in vitro study. While many cell processes during migration through 3D matrices are similar to those required for movement across rigid 2D surfaces, including adherence, migration through ECM also requires cells...
10:24
Preparation of 3D Collagen Gels and Microchannels for the Study of 3D Interactions In Vivo
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Cited by 16 •
2016
Collagen is a core component of the ECM, and provides essential cues for several cellular processes ranging from migration to differentiation and proliferation. Provided here is a protocol for embedding cells within 3D collagen hydrogels, and a more advanced technique for generating randomized or aligned collagen matrices using PDMS microchannels.
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.
06:39
Generation of Tissue Spheroids via a 3D Printed Stamp-Like Device
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Cited by 2 •
2022
The present protocol describes a technique to produce tissue spheroids on a large scale cost-effectively using a 3D printed stamp-like device.
FAQs
Segmentation separates selected anatomical regions from voxel-based CT or MRI data and represents them as measurable structures. This conversion supports analysis beyond viewing raw images, because researchers and clinicians can work with identified anatomy for visualization, surface-model reconstruction, or quantitative assessment. The resulting structures can also contribute to planning and evaluation workflows in medical imaging.
Registration provides a processing step for relating imaging data within a study or across imaging datasets. In combination with segmentation and visualization modules, it helps organize anatomical information for analysis, treatment evaluation, and image-guided procedures. Its value is greatest when researchers need to examine spatial relationships or compare information generated from biomedical imaging.
Volume rendering presents voxel data as a three-dimensional visual representation, while surface-model reconstruction converts segmented anatomical information into geometric surfaces. These outputs serve different purposes: rendering supports interpretation of the original imaging volume, whereas surface models provide a measurable representation of selected anatomy. Using either approach depends on whether the workflow emphasizes image visualization or anatomical modeling.
DICOM provides the pathway for importing imaging studies such as CT and MRI into the platform. Once imported, the data can be processed through modules for segmentation, registration, volume rendering, and reconstruction. This workflow connects acquired clinical imaging with downstream visualization, analysis, surgical planning, and research tasks without treating the images as isolated files.
Its extensible architecture allows users to develop specialized workflows and integrate computational tools with existing image-processing capabilities. This makes the platform adaptable when a project requires more than standard visualization or analysis. In biomedical research, such customization can support investigations of anatomy and disease across diverse imaging modalities while keeping related processing steps within one environment.
The platform supports surgical planning, image-guided procedures, quantitative analysis, and treatment evaluation. Segmentation and reconstructed anatomical models can help represent structures relevant to planning, while visualization and processing support interpretation during research or clinical preparation. These uses make the software relevant whenever three-dimensional biomedical data must be examined, measured, or incorporated into a medical decision workflow.
By converting imaging voxels into identified, measurable anatomical structures, the platform provides a basis for quantitative analysis rather than visual inspection alone. Researchers can combine segmentation, registration, and reconstruction to investigate anatomy or disease and assess treatment-related imaging information. The same capabilities also support biomedical studies that compare or characterize findings across diverse imaging modalities.