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

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{{documentation/{{documentation/version}}/extension-section|Extension Description}}
 
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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. In this study we aim to multimodal application of MRI for evaluation robust and effective of MS lesions as well as appearing white matter healthy (NAWM). Weighted images T1, T2 and FLAIR are widely used for the diagnosis of disease and recently, diffusion tensor imaging (DTI) add another source of useful information for the diagnosis of MS. However, main barrier is the low signal to noise ratio (SNR) existing in such
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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.  
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imaging techniques, which eventually diminish the efficiency of the method of segmentation. We propose the application of anomalous anisotropic filtering (ADF)
 
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an important tool in improving the SNR and thus increased precision segmentation and detection of MS lesions and NAWM. This project sees one multicentric interaction and the use of innovative and optimized tools for the current lesions targeting challenge of patients with MS. the improvement is-searching
 
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definition of brain tissue separation, finer detection of the disease and the establishment of a functional computational tool for clinical application in the diagnosis of MS.
 
  
 
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{{documentation/{{documentation/version}}/extension-section|Modules}}
 
{{documentation/{{documentation/version}}/extension-section|Modules}}
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*[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
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*Support Modules
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*[[Documentation/{{documentation/version}}/Modules/BrainTissuesMask|Brain Tissues Mask]]
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**[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
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*[[Documentation/{{documentation/version}}/Modules/MSLesionTrack|Multiple Sclerosis Lesion Track]]
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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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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.

  • DTI FA wm segmented.png

    DTI-FA map with the white matter segmented

  • DTI FA lesions.png

    MS lesions segmented from the DTI-FA maps (using statistical approach)

Tutorials

Similar Extensions

N/A

References

Information for Developers

Work in progress
 Section under construction.
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