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3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support

  • Ron Kikinis
  • Steve D. Pieper
  • Kirby G. Vosburgh
Chapter

Abstract

3D Slicer is an open-source platform for the analysis and display of information derived from medical imaging and similar data sets. Such advanced software environments are in daily use by researchers and clinicians and in many nonmedical applications. 3D Slicer is unique through serving clinical users, multidisciplinary clinical research terms, and software architects within a single technology structure and user community. Functions such as interactive visualization, image registration, and model-based analysis are now being complemented by more advanced capabilities, most notably in neurological imaging and intervention. These functions, originally limited to offline use by technical factors, are integral to large scale, rapidly developing research studies, and they are being increasingly integrated into the management and delivery of care. This activity has been led by a community of basic, applied, and clinical scientists and engineers, from both academic and commercial perspectives. 3D Slicer, a free open-source software package, is based in this community; 3D Slicer provides a set of interactive tools and a stable platform that can quickly incorporate new analysis techniques and evolve to serve more sophisticated real-time applications while remaining compatible with the latest hardware and software generations of host computer systems.

Keywords

Volume Rendering Iterative Close Point Iterative Close Point Diffusion Magnetic Resonance Imaging Source Code Repository 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors thank the 3D Slicer development community members for their efforts and recognize with gratitude the support of the NIH through NAMIC, NAC, NCIGT, and portions of many other projects. The authors were supported by the Neuroimage Analysis Center (NAC), an NIBIB Resource Center, NIH NIBIB P41 grant EB015902, and the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005419. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics. Drs. Kikinis and Vosburgh were supported in part by the National Center for Image-Guided Therapy, NIH P41 EB015898. Dr. Vosburgh also received support from the Center for Integration of Medicine and Innovative Technology (CIMIT).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ron Kikinis
    • 1
  • Steve D. Pieper
    • 2
  • Kirby G. Vosburgh
    • 1
  1. 1.National Center for Image Guided Therapy, Department of RadiologyBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA
  2. 2.Surgical Planning LaboratoryIsomics IncorporatedCambridgeUSA

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