Difference between revisions of "Documentation/4.2/Extensions/TubeTK"

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Author: Stephen Aylward<br>
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Contributors: Jean-Christophe Fillion-Robin (Kitware), Christopher Mullins (Kitware), Michael Jeulin-L (Kitware), Matthew McCormick (Kitware)<br>
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Acknowledgments:
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This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by ... <br>
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Author: Stephen Aylward (Kitware)<br>
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Contributors: Jean-Christophe Fillion-Robin ({{collaborator|name|kitware}}), Christopher Mullins ({{collaborator|name|kitware}}), Michael Jeulin-L ({{collaborator|name|kitware}}), Matthew McCormick ({{collaborator|name|kitware}})<br>
 
Contact: Christopher Mullins, <email>christopher.mullins@kitware.com</email><br>
 
Contact: Christopher Mullins, <email>christopher.mullins@kitware.com</email><br>
 
Website: http://www.tubetk.org/<br>
 
Website: http://www.tubetk.org/<br>
 
License: [http://tubetk.org/tubetk/project/license.html TubeTK License]
 
License: [http://tubetk.org/tubetk/project/license.html TubeTK License]
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Tubes and surfaces, as generalized 1D and 2D manifolds in N-dimensional images, are essential components in a variety of image analysis tasks.    Instances of tubular structures in images include blood vessels in magnetic resonance angiograms and b-mode ultrasound images, wires in microscopy images of integrated circuits, roads in areal photographs, and nerves in confocal microscopy.
 
Tubes and surfaces, as generalized 1D and 2D manifolds in N-dimensional images, are essential components in a variety of image analysis tasks.    Instances of tubular structures in images include blood vessels in magnetic resonance angiograms and b-mode ultrasound images, wires in microscopy images of integrated circuits, roads in areal photographs, and nerves in confocal microscopy.
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==Longterm Goals==
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By focusing on local geometric structure, the algorithms are able to accomplish segmentations, registrations, and other analyses that consider the physicial properties of objects and their variations, while not requiring limiting assumptions on the specific arrangement or general shape of the objects in the images. We are applying these techniques to push image understanding in new directions such as:
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* registration of abdominal images even when organs slides against one another
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* forming statistical atlases of intra-canrial vessel network topology even when that topology changes between subjects
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* segmentation of arbitrary objects in images even when intensity statistics of those objects, and the objects around them, vary from image to image.
  
 
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*[[Documentation/{{documentation/version}}/Modules/tubeSampleCLIApplication|TubeTK Sample CLI Application]]
 
*[[Documentation/{{documentation/version}}/Modules/tubeSampleCLIApplication|TubeTK Sample CLI Application]]
 
*[[Documentation/{{documentation/version}}/Modules/tubeSkeletonize|TubeTK Skeletonize]]
 
*[[Documentation/{{documentation/version}}/Modules/tubeSkeletonize|TubeTK Skeletonize]]
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{{documentation/{{documentation/version}}/extension-section|Acknowledgements}}
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The development of TubeTK is being funded, in part, by the following grants
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* 1. NIH/NIBIB sponsored "National Alliance of Medical Image Computing" (NA-MIC, PI: Kikinis) project
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**1U54EB005149-01
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**[http://www.na-mic.org NA-MIC]
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* 2. DARPA sponsored "Trust in Integrated Circuits" (EXPOSE, PI: Bajura) program
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** USC/ISI Team
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** [http://www.darpa.mil DARPA]
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* 3. NIH/NCI sponsored "Image Registration for Ultrasound-Based Neurosurgical Navigation" (NeuralNav, PI: Aylward, Wells)
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** 1R01CA138419-01
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** [http://public.kitware.com/Wiki/NeuralNav NeuralNav]

Latest revision as of 07:43, 14 June 2013

Home < Documentation < 4.2 < Extensions < TubeTK


For the latest Slicer documentation, visit the read-the-docs.



Introduction and Acknowledgements

Acknowledgments: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by ...
Author: Stephen Aylward (Kitware)
Contributors: Jean-Christophe Fillion-Robin (Kitware), Christopher Mullins (Kitware), Michael Jeulin-L (Kitware), Matthew McCormick (Kitware)
Contact: Christopher Mullins, <email>christopher.mullins@kitware.com</email>
Website: http://www.tubetk.org/
License: TubeTK License

National Alliance for Medical Image Computing (NA-MIC)  

Extension Description

TubeTK is an open-source toolkit for the segmentation, registration, and analysis of tubes and surfaces in images.

Tubes and surfaces, as generalized 1D and 2D manifolds in N-dimensional images, are essential components in a variety of image analysis tasks. Instances of tubular structures in images include blood vessels in magnetic resonance angiograms and b-mode ultrasound images, wires in microscopy images of integrated circuits, roads in areal photographs, and nerves in confocal microscopy.

Longterm Goals

By focusing on local geometric structure, the algorithms are able to accomplish segmentations, registrations, and other analyses that consider the physicial properties of objects and their variations, while not requiring limiting assumptions on the specific arrangement or general shape of the objects in the images. We are applying these techniques to push image understanding in new directions such as:

  • registration of abdominal images even when organs slides against one another
  • forming statistical atlases of intra-canrial vessel network topology even when that topology changes between subjects
  • segmentation of arbitrary objects in images even when intensity statistics of those objects, and the objects around them, vary from image to image.

Modules

Acknowledgements

The development of TubeTK is being funded, in part, by the following grants

  • 1. NIH/NIBIB sponsored "National Alliance of Medical Image Computing" (NA-MIC, PI: Kikinis) project
  • 2. DARPA sponsored "Trust in Integrated Circuits" (EXPOSE, PI: Bajura) program
  • 3. NIH/NCI sponsored "Image Registration for Ultrasound-Based Neurosurgical Navigation" (NeuralNav, PI: Aylward, Wells)
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