Documentation/Nightly/Extensions/MSLesionTrack - Revision history

Acsenrafilho at 13:42, 30 April 2016

2016-04-30T13:42:10Z

Acsenrafilho at 22:43, 22 April 2016

2016-04-22T22:43:07Z

Acsenrafilho at 22:33, 22 April 2016

2016-04-22T22:33:57Z

Acsenrafilho: Created page with "{{documentation/versioncheck}} {{documentation/{{documentation/version}}/module-header}}

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 {{documentation/{{documentation/version}}/extension-section|Extension Description}}
 [[Image:MSLesionTrackExtension-logo.png|left]]
-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
+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.
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/extension-section|Modules}}
-*[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
+*Support Modules
-*[[Documentation/{{documentation/version}}/Modules/BrainTissuesMask|Brain Tissues Mask]]
+**[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
-*[[Documentation/{{documentation/version}}/Modules/MSLesionTrack|Multiple Sclerosis Lesion Track]]
+**[[Documentation/{{documentation/version}}/Modules/BrainTissuesMask|Brain Tissues Mask]]
 <!-- ---------------------------- -->

@@ Line 25: / Line 25: @@
 {{documentation/{{documentation/version}}/extension-section|Extension Description}}
 [[Image:MSLesionTrackExtension-logo.png|left]]
-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:
+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
+imaging techniques, which eventually diminish the efficiency of the method of segmentation. We propose the application of anomalous anisotropic filtering (ADF)
-*B-spline method for deformable image registration (GPU and multicore accelerated)
+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
-*Demons method for deformable image registration (GPU accelerated)
+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.
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/extension-section|Modules}}
-*[[Documentation/{{documentation/version}}/Modules/PlmBSplineDeformableRegistration|Plastimatch Automatic deformable image registration]]
+*[[Documentation/{{documentation/version}}/Modules/BrainExtractionTool|Brain Extraction Tool]]
-*[[Documentation/{{documentation/version}}/Modules/PlmDICOMRTImport|Plastimatch DICOM-RT import]]
+*[[Documentation/{{documentation/version}}/Modules/BrainTissuesMask|Brain Tissues Mask]]
-*[[Documentation/{{documentation/version}}/Modules/PlmLANDWARP|Plastimatch LANDWARP Landmark]]
+*[[Documentation/{{documentation/version}}/Modules/MSLesionTrack|Multiple Sclerosis Lesion Track]]
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/extension-section|Use Cases}}
 [http://plastimatch.org/data_sources.html Sample data] to use with modules.
-<gallery widths="200px" perrow="4">
+<gallery widths="400px" perrow="2">
-Image:plastimatch_dicomrt_ss.png|DICOM-RT Structure Set
+Image:DTI_FA_wm_segmented.png|DTI-FA map with the white matter segmented
 </gallery>
@@ Line 54: / Line 48: @@
 <gallery widths="200px" perrow="4">
-Image:plastimatch_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1023/3D_Slicer_Plastimatch_Registration_Tutorial.ppt Download tutorial]
+Image:mslesiontrackextension_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1023/3D_Slicer_Plastimatch_Registration_Tutorial.ppt Download tutorial]
 </gallery>
@@ Line 63: / Line 57: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/extension-section|References}}
-* 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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 {{documentation/{{documentation/version}}/module-introduction-row}}
 Acknowledgments:
-This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by ... <br>
+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>
+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>
+Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email><br>
 {{documentation/{{documentation/version}}/module-introduction-row}}
 {{documentation/{{documentation/version}}/module-introduction-logo-gallery
-|{{collaborator|logo|namic}}|{{collaborator|longname|namic}}
+|Image:CSIM-logo.png|CSIM Laboratory
 }}
 {{documentation/{{documentation/version}}/module-introduction-end}}
@@ Line 24: / Line 24: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/extension-section|Extension Description}}
 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: