Difference between revisions of "Documentation/Nightly/Modules/BasicBrainTissues"

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{{documentation/{{documentation/version}}/module-section|Module Description}}
 
{{documentation/{{documentation/version}}/module-section|Module Description}}
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This module offer a...
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This module offer a simple and robust brain tissue segmentation focused on white matter, gray matter and CSF brain tissues. The method applied here is based on K-Means clustering segmentation, which presents best results with high quality T1 weighted MRI images.
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'''NOTE''': This module can be used alone (only apply the K-Means segmentation method on the input data), but the [[Documentation/{{documentation/version}}/Modules/BrainStructuresSegmenter|Brain Structures Segmenter]] module already use it internally, which control a image processing pipeline for a better brain tissue segmentation result.
  
 
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{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
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* Use Case 1: a
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* Use Case 1: Separate White Matter, Gray Matter and CSF brain tissues from a strucutral MRI image.
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**b
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**There are some image processing strategies that may need a specific brain tissue mask and this module could facilitate this task. For instance, a Multiple Sclerosis lesion detection algorithm may need a White Matter mask in order to define a localized brain region where the lesion are more probable to appear.
 
<gallery widths="200px" perrow="3">
 
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Image:MRI_raw.png|Raw T1 weighted MRI Image
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Image:T1_tissues.png|White matter, gray matter and CSF tissues segmented from the previous MRI image
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Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
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Image:WM_3DReconstruction.png|A white matter mask 3D reconstruction
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Image:GM_3DReconstruction.png|A gray matter mask 3D reconstruction
 
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}
 
{{documentation/{{documentation/version}}/module-section|Panels and their use}}
  
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[[Image:aad_scalar_gui.png|thumb|380px|User Interface]]
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[[Image:basicbraintissues_gui.png|thumb|500px|User Interface]]
 
IO:
 
IO:
 
*Input Volume
 
*Input Volume
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**Select the input image
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**Input volume. The algorithm works better with high resolution T1 MRI images alread brain extract and inhomogeneity corrected
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*Output Volume
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*Image Modality
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**Set the output image file which the filters should place the final result
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**Select the image modality inserted as a input volume
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*Brain Mask
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**Output brain tissue mask
  
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Diffusion Parameters:
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Tissue Type Output:
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*Conductance
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*Separate one tissue class
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**The conductance regulates the diffusion intensity in the neighbourhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
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**Choose if you want all the tissues classes or only one class segmented
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*Number of Iteractions
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*Tissue
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**The number of iterations regulates the numerical simulation of the anomalous process over the image. This parameters is also related with the de-noising intensity, however it is more sensible to the noise intensity. Choose the higher number of iterations if the image presents high intensity noise which is not well treated by the conductance parameter.
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**Choose what is the brain tissue label that you want as the output label
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*Time Step
 
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**The time step is a normalization parameters for the numerical simulation. The maximum value, given as default, is set to 3D images. Lower time step restrict the numerical simulation of the anomalous process.
 
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*Anomalous parameter
 
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**The anomalous parameter (or q value) is the generalization parameters responsible to give the anomalous process approach on the diffusion equation. See the reference paper<ref>Da S Senra Filho, A. C., Garrido Salmon, C. E., & Murta Junior, L. O. (2015). Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), 2355–2373. doi:10.1088/0031-9155/60/6/2355</ref> to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).
 
  
 
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
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*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]
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*[https://www.slicer.org/wiki/Modules:EMSegmenter-3.6 EM Segmenter]
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*[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion|Gradient Anisotropic Image Filter]]
 
  
 
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{{documentation/{{documentation/version}}/module-section|References}}
 
{{documentation/{{documentation/version}}/module-section|References}}
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* paper
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N/A
  
 
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Revision as of 14:02, 27 November 2016

Home < Documentation < Nightly < Modules < BasicBrainTissues


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


Introduction and Acknowledgements

Extension: BrainTissuesExtension
Webpage: http://dcm.ffclrp.usp.br/csim/
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  
CAPES Brazil  

Module Description

This module offer a simple and robust brain tissue segmentation focused on white matter, gray matter and CSF brain tissues. The method applied here is based on K-Means clustering segmentation, which presents best results with high quality T1 weighted MRI images.

NOTE: This module can be used alone (only apply the K-Means segmentation method on the input data), but the Brain Structures Segmenter module already use it internally, which control a image processing pipeline for a better brain tissue segmentation result.

Use Cases

  • Use Case 1: Separate White Matter, Gray Matter and CSF brain tissues from a strucutral MRI image.
    • There are some image processing strategies that may need a specific brain tissue mask and this module could facilitate this task. For instance, a Multiple Sclerosis lesion detection algorithm may need a White Matter mask in order to define a localized brain region where the lesion are more probable to appear.

  • White matter, gray matter and CSF tissues segmented from the previous MRI image

  • A white matter mask 3D reconstruction

  • A gray matter mask 3D reconstruction


Panels and their use

User Interface

IO:

  • Input Volume
    • Input volume. The algorithm works better with high resolution T1 MRI images alread brain extract and inhomogeneity corrected
  • Image Modality
    • Select the image modality inserted as a input volume
  • Brain Mask
    • Output brain tissue mask

Tissue Type Output:

  • Separate one tissue class
    • Choose if you want all the tissues classes or only one class segmented
  • Tissue
    • Choose what is the brain tissue label that you want as the output label

Similar Modules

References

N/A

Information for Developers

Work in progress
 Section under construction.


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