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3D Slicer

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3DSlicer
Original author(s)The Slicer Community
Stable release
3.4 / May, 2009
Operating systemCross-platform
Available inC++, Tcl, Python, Java
TypeScientific visualization and image computing
LicenseBSD-style
Websitewww.slicer.org

3D Slicer (Slicer) is a free, open source software package for scientific visualization and image analysis[1]. Slicer is used in a variety of medical applications, including autism, multiple sclerosis, systemic lupus erythematosus, prostate cancer, schizophrenia, orthopedic biomechanics, COPD, cardiovascular disease and neurosurgery.

About Slicer

3D Slicer is a flexible platform that can be easily extended to enable development of both interactive and batch processing tools for a variety of applications.

3D Slicer provides image registration, processing of DTI (diffusion tractography), an interface to external devices for image guidance support, and GPU-enabled volume rendering, among other capabilities. 3D Slicer has a modular organization that allows the easy addition of new functionality and provides a number of generic features not available in competing tools.

The interactive visualization capabilities of 3D Slicer include the ability to display arbitrarily oriented image slices, build surface models from image labels, and high performance and high performance volume rendering. 3D Slicer also supports a rich set of annotation features (fiducials and measurement widgets, customized colormaps).

Slicer's capabilities include[2]:

  • 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 image segmentation
  • Analysis and visualization of diffusion tensor imaging data
  • Tracking of devices for image-guided procedures.

Slicer is compiled for use on multiple platforms, including Windows, Linux, and Mac OS X.

Slicer is distributed under a BSD style, free, open source license. The license has no restrictions on use of the software. However, no claims are made on the software being useful for any particular task. It is entirely the responsibility of the user to ensure compliance with local rules and regulations. Slicer has not been approved for clinical use in the US or elsewhere.

History

Slicer started as a masters thesis project between the Surgical Planning Laboratory at the Brigham and Women's Hospital and the MIT Artificial Intelligence Laboratory in 1998[3]. 3D Slicer version 2 has been downloaded several thousand times. In 2007 a completely revamped version 3 of Slicer was released. This software has enabled a variety of research publications, all aimed at improving image analysis[4].

This significant software project has been enabled by the participation of several large-scale NIH funded efforts, including the NA-MIC, NAC, BIRN, CIMIT, Harvard Catalyst and NCIGT communities. The funding support comes from several federal funding sources, including NCRR, NIBIB, NIH Roadmap, NCI, NSF and the DOD.

Users

Left: 3D rendering, right: Open MR system

Slicer's platform provides functionalities for segmentation, registration and three-dimensional visualization of multimodal image data, as well as advanced image analysis algorithms for diffusion tensor imaging, functional magnetic resonance imaging and image-guided radiation therapy. Standard image file formats are supported, and the application integrates interface capabilities to biomedical research software.

Slicer has been used in a variety of clinical research. In image-guided therapy research, Slicer is frequently used to construct and visualize collections of MRI data that are available pre- and intra-operatively to allow for the acquiring of spatial coordinates for instrument tracking[5]. In fact, Slicer has already played such a pivotal role in image-guided therapy, it could be thought of as growing up alongside that field, with over 200 publications referencing Slicer since 1998[6].

In addition to producing 3D models from conventional MRI images, Slicer has also been used to present information derived from fMRI (using MRI to assess blood flow in the brain related to neural or spinal cord activity)Cite error: A <ref> tag is missing the closing </ref> (see the help page).. Neuroimage. 2007 Apr 1;35(2):609-624. PMID: 17289403.</ref>, DTI (using MRI to measure the restricted diffusion of water in imaged tissue)[7], and electrocardiography[8]. For example, Slicer's DTI package allows the conversion and analysis of DTI images. The results of such analysis can be integrated with the results from analysis of morphologic MRI, MR angiograms and fMRI. Slicer has also been used in paleonotology[9].

Developers

Slicer is based on VTK, a graphical library that provides a high-level interface to OpenGL and a pipeline mechanism to connect graphical filters. The library is implemented in C++ but provides a Tcl wrapper to instantiate and execute its methods. Tcl/Tk comprises the rest of 3D Slicer user interface and event handling.

Slicer software supports automatic testing and employs an extreme programming approach with nightly builds natively on multiple platforms. Recent accomplishments include added capability for plugging in external modules using XML-based command line interface.

Slicer.org offers resources for those who would like to improve Slicer designs and applications. Various tutorials and instructions, along with a community of other developers can be found there.

Components of Slicer

References

  1. ^ Pieper S., Halle M., Kikinis R. 3D SLICER. Proceedings of the 1st IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2004; 1:632-635.
  2. ^ Pieper S., Lorensen B., Schroeder W., Kikinis R. The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006; 1:698-701.
  3. ^ Hirayasu Y., Shenton M.E., Salisbury D.F., Dickey C.C., Fischer I.A., Mazzoni P., Kisler T., Arakaki H., Kwon J.S., Anderson J.E., Yurgelun-Todd D., Tohen M., McCarley R.W. Lower left temporal lobe MRI volumes in patients with first-episode schizophrenia compared with psychotic patients with first-episode affective disorder and normal subjects. Am J Psychiatry. 1998 Oct;155(10):1384-91. PMID: 9766770.
  4. ^ Pieper S., Lorensen B., Schroeder W., Kikinis R. The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006; 1:698-701.
  5. ^ Hata N., Pieper S., Jolesz F.A., Tempany C.M., Black P.M., Morikawa S., Iseki H., Hashizume M., Kikinis R. Application of Open Source Image Guided Therapy Software in MR-guided Therapies. Int Conf Med Image Comput Comput Assist Interv. 2007;10(Pt 1):491-8. PMID: 18051095.
  6. ^ For a list of publications citing Slicer usage since 1998, visit: http://www.slicer.org/publications/pages/display/?collectionid=11
  7. ^ Ziyan U., Tuch D., Westin C-F. Segmentation of Thalamic Nuclei from DTI Using Spectral Clustering. Int Conf Med Image Comput Comput Assist Interv. 2006;9(Pt 2):807-14. PMID: 17354847.
  8. ^ Verhey J.F., Nathan N.S., Rienhoff O., Kikinis R., Rakebrandt F., D'Ambra M.N. Finite-element-method (FEM) model generation of time-resolved 3D echocardiographic geometry data for mitral-valve volumetry. Biomed Eng Online. 2006 Mar 3;5:17. PMID: 16512925. PMCID: PMC1421418.
  9. ^ http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-vi.html
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