Difference between revisions of "Documentation/Nightly/Extensions/MSLesionTrack"

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Acknowledgments:
 
Acknowledgments:
This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by ... <br>
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This work was partially funded by CAPES and CNPq, a Brazillian Agencies. Information on CAPES can be obtained on the [http://www.capes.gov.br/ CAPES website] and [http://www.cnpq.br/ CNPq website] <br>
Author:Greg Sharp<br>
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Author:Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)<br>
Contributors: Jean-Christophe Fillion-Robin ({{collaborator|name|kitware}})<br>
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Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email><br>
Contact: Greg Sharp, <email>gcsharp@partners.org</email><br>
 
Website: http://plastimatch.org/<br>
 
License: [http://forge.abcd.harvard.edu/gf/project/plastimatch/scmsvn/?action=browse&path=%2F*checkout*%2Fplastimatch%2Ftrunk%2Fsrc%2Fplastimatch%2FLICENSE.TXT&revision=2388 BSD]
 
 
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|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
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|Image:CSIM-logo.png|CSIM Laboratory
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|Image:USP-logo.png|University of Sao Paulo
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|Image:CNPq-logo.png|CNPq Brazil
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|Image:CAPES-logo.png|CAPES Brazil
 
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Plastimatch is an open source software for image computation. Our main focus is high-performance volumetric registration of medical images, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Software features include:
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[[Image:MSLesionTrackExtension-logo.png|left]]
 
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Multiple sclerosis (MS) is a degenerative neurological disease growing relevance. The segmentation of lesions on magnetic resonance imaging (MRI) and its boundaries with healthy tissue remains a challenge for correct diagnosis of MS patients. Currently, many imaging methods Magnetic resonance imaging have been applied to this problem, but with success modest.The diffusion tensor imaging (DTI) have been discussed as an important imaging technique which could be useful for the diagnosis of MS. However, main barrier is the low signal to noise ratio (SNR) existing in such imaging techniques, which eventually diminish the efficiency of the method of segmentation. This extension aims to provide image processing tools in order to segment and detect MS lesions and the surrounding NAWM in the patient disease progression.
*B-spline method for deformable image registration (GPU and multicore accelerated)
 
*Demons method for deformable image registration (GPU accelerated)
 
*ITK-based algorithms for translation, rigid, affine, demons, and B-spline registration
 
*Pipelined, multi-stage registration framework with seamless conversion between most algorithms and transform types
 
*Landmark-based deformable registration using thin-plate splines for global registration
 
*Landmark-based deformable registration using radial basis functions for local corrections
 
*Broad support for 3D image file formats (using ITK), including DICOM, Nifti, NRRD, MetaImage, and Analyze
 
*DICOM and DICOM-RT import and export
 
*XiO import and export
 
  
 
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{{documentation/{{documentation/version}}/extension-section|Modules}}
 
{{documentation/{{documentation/version}}/extension-section|Modules}}
*[[Documentation/{{documentation/version}}/Modules/PlmBSplineDeformableRegistration|Plastimatch Automatic deformable image registration]]
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*Support Modules
*[[Documentation/{{documentation/version}}/Modules/PlmDICOMRTImport|Plastimatch DICOM-RT import]]
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**[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
*[[Documentation/{{documentation/version}}/Modules/PlmLANDWARP|Plastimatch LANDWARP Landmark]]
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**[[Documentation/{{documentation/version}}/Modules/BrainTissuesMask|Brain Tissues Mask]]
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*Lesion Segmentation Modules
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**[[Documentation/{{documentation/version}}/Modules/DTILesionTrack|DTI Lesion Track]]
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**[[Documentation/{{documentation/version}}/Modules/LongitudinalDTILesionTrack|Longitudinal DTI Lesion Track]]
  
 
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[http://plastimatch.org/data_sources.html Sample data] to use with modules.
 
[http://plastimatch.org/data_sources.html Sample data] to use with modules.
<gallery widths="200px" perrow="4">
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<gallery widths="400px" perrow="2">
Image:plastimatch_dicomrt_ss.png|DICOM-RT Structure Set
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Image:DTI_FA_wm_segmented.png|DTI-FA map with the white matter segmented
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Image:DTI_FA_lesions.png|MS lesions segmented from the DTI-FA maps (using statistical approach)
 
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Image:plastimatch_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1023/3D_Slicer_Plastimatch_Registration_Tutorial.ppt Download tutorial]
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Image:mslesiontrackextension_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1023/3D_Slicer_Plastimatch_Registration_Tutorial.ppt Download tutorial]
 
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{{documentation/{{documentation/version}}/extension-section|References}}
 
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* G Sharp, N Kandasamy, H Singh, M Folkert, "GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration," Physics in Medicine and Biology, 52(19), pp 5771-83, 2007.
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Latest revision as of 13:42, 30 April 2016

Home < Documentation < Nightly < Extensions < MSLesionTrack


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


Introduction and Acknowledgements

Acknowledgments: This work was partially funded by CAPES and CNPq, a Brazillian Agencies. Information on CAPES can be obtained on the CAPES website and CNPq website
Author:Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email>

CSIM Laboratory  
University of Sao Paulo  
CNPq Brazil  
CAPES Brazil  


Extension Description

MSLesionTrackExtension-logo.png

Multiple sclerosis (MS) is a degenerative neurological disease growing relevance. The segmentation of lesions on magnetic resonance imaging (MRI) and its boundaries with healthy tissue remains a challenge for correct diagnosis of MS patients. Currently, many imaging methods Magnetic resonance imaging have been applied to this problem, but with success modest.The diffusion tensor imaging (DTI) have been discussed as an important imaging technique which could be useful for the diagnosis of MS. However, main barrier is the low signal to noise ratio (SNR) existing in such imaging techniques, which eventually diminish the efficiency of the method of segmentation. This extension aims to provide image processing tools in order to segment and detect MS lesions and the surrounding NAWM in the patient disease progression.

Modules

Use Cases

Sample data to use with modules.

Tutorials

Similar Extensions

N/A

References

Information for Developers