3D Slicer – platform for medical image informatics, image processing, and three-dimensional visualization

3D Slicer (Slicer) is a multi-platform, open source, integrated software for visualization and image computing. It’s intended for computer scientists and clinical researchers.

The software is extremely well featured. It has functionality for loading, viewing, analyzing, processing and interacting with biomedical data, for segmentation, registration and three-dimensional visualization of multi-modal image data, as well as advanced image analysis algorithms for diffusion tensor imaging, functional magnetic resonance imaging and image-guided therapy. Standard image file formats are supported, and the application integrates interface capabilities to biomedical research software and image informatics frameworks.

Slicer can be extended at run-time with plug-in modules, allowing developers and researchers to customise and specialize Slicer for a specific purpose.

Slicer is used in a variety of medical applications including neurosurgery, prostate cancer, cardiovascular disease, autism, multiple sclerosis, systemic lupus erythematosus, schizophrenia, orthopedic biomechanics, and COPD.

Slicer is based on VTK, a graphical library that provides a high-level interface to OpenGL and a pipeline mechanism to connect graphical filters.

Features include:

  • Sophisticated complex visualization capabilities.
  • Scene snapshots allow capture of all visualization parameters of a scene.
  • Extensive support for IGT and diffusion tensor imaging.
  • Advanced registration / data fusion capabilities.
  • Comprehensive I/O capabilities.
  • Reading and writing DICOM images and a variety of other formats.
  • Interactive visualization of images, triangulated 3D surface models, and volume renderings.
  • Manual editing.
  • Fusion and co-registering of data using rigid and non-rigid algorithms.
  • Automatic segmentation.
  • Analysis and visualization of diffusion tensor imaging data.
  • Tracking of devices for image-guided procedures.
  • Change Tracker tool for quantification of the subtle changes in pathology.
  • Volume Rendering.
  • FetchMI (Fetch Medial Informatics).
  • Large set of modules/filters:
    • Informatics: FetchMI, QdecModule, QueryAtlas.
    • Registration: ACPC Transform, Affine registration, Deformable BSpline registration, Diffeomorphic Demons Algorithm, Linear registration, Register images, Rigid registration.
    • Segmentation: EMSegment Template Builder, EMSegment Command-line, EMSegment Simple, Gyri Contour Segmentation, Otsu Threshold Segmentation, Simple region growing.
    • Statistics: LabelStatistics.
    • Diffusion: Diffusion Tensor Estimation, Diffusion Tensor Scalar Measurements, Python Stochastic Tractography, Resample DTI Volume, Filtering.
    • Tractography: FibertBundles, FiducialSeeding, Labelmap Seeding.
    • IGT: NeuroNav, OepnIGTLink IF, ProstateNav.
    • Filtering: GradientAnisotropicFilter, CheckerBoard Filter, Extract Skeleton, Histogram Matching, Image Label Combine, Otsu Threshold, Python Gaussian Smoothing, Python Gradient Anisotropic Diffusion, Resample Scalar Volume, Resample Scalar/Vector/DWI Volume, Threshold Image, Voting Binary Hole Filling, Zero Crossing Based Edge Detection Filter, Arithmetic, Denoising, Morphology.
    • Surface Modules: ClipModel, ModelIntoLabelVolume, FreesurferSurfaceSectionExtraction, Grayscale Model Maker, Label Map Smoothing, Model Maker, Python Surface Connectivity, Python Surface ICP Registration, Python Surface Toolbox.
    • Batch Processing: EMSegment BatchMake, Gaussian Blur BatchMake, Register Images BatchMake, Resample Scalar Volume BatchMake.
    • Converters: ExtractSubvolume, Create a DICOM Series, Dicom DWI loader, Dicom to Nrrd, Orient Images, Python Binarize Map, Python Convert Fiducials to Labelmap, Python Convert Volume to NUMPY File, Python Create Single ROI file, Python Explode Volume Transform, Python Load Volume from NUMPY File, Python Resample Volume, Python Reslice As Volume.
    • Developer Tools: ScriptedModuleExample, Execution Model Tour, Python Numpy Script, Python Script.
  • ROI seeding.

Website: www.slicer.org
Support: Documentation
Developer: The Slicer Community
License: BSD License

3D Slicer in action
Click image for full size

3D Slicer is written in C++ and Python. Learn C++ with our recommended free books and free tutorials. Learn Python with our recommended free books and free tutorials.

Return to Medical Imaging

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