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

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{{documentation/{{documentation/version}}/module-section|Module Description}}
 
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This module offer a ...
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[[Image:BrainStructuresSegmenter-icon.png|right]]
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This module offers a general brain segmentation pipeline, calling different algorithms depending on the tissue type that the user wants to segment. The list below illustrates the segmentation methods available at moment:
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* [[Documentation/{{documentation/version}}/Modules/BasicBrainTissues|Basic Brain Tissues]]: A general brain tissues segmentation focused on the white matter, gray matter and CSF brain tissue segmentation.
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* [[Documentation/{{documentation/version}}/Modules/BrainLogisticSegmentation|Brain Logistic Classification (BLS)]]: A recent brain segmentation method focused on global tissue segmentation, usually CSF, gray and white matter delineations. general brain tissues segmentation focused on the white matter, gray matter and CSF brain tissues segmentation.<ref>BLS paper</ref>
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'''NOTE''': Each segmentation method listed are related to the segmentation part of the image processing pipeline controlled by this module, i.e. the Brain Structure Segmenter module apply a set of image processing algorithms in which, in the Segmentation part, uses the above methods. The user still has the flexibility to choose what pre and post processing that should be applied in the general image processing pipeline.  
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The diagram below illustrates the image processing pipeline used in this module
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[[File:SegmentationDiagram_BrainsStructuresSegmenter.png|800px|thumb|center|Segmentation pipeline applied internally in this module]]
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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: Tissue classification
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**b
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**There are several image quantitative approaches that use only a certain tissue type (for instance, cortical thickness) in which a previous brain segmentation could be needed.
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<gallery widths="200px" perrow="3">
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**A simple brain tissue mask could be obtained from this module (WM, GM and CSF are available at moment).
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Image:MRI_raw.png|Raw T1 weighted MRI Image
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Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
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<gallery widths="300px" heights="300px">
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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: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:BrainStructuresSegmenter_gui.png|thumb|480px|User Interface]]
 
IO:
 
IO:
 
*Input Volume
 
*Input Volume
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**Select the input image
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**Pick the input to the algorithm. This should be an MRI strutural images with a type listed in the Image Modality option
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*Image Modality
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**MRI strutural image inserted as a input volume
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*Is brain extracted?
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**Is the input data already brain extracted? If not, the [https://www.slicer.org/wiki/Documentation/Nightly/Modules/ROBEXBrainExtraction ROBEX] brain extraction method is used
 
*Output Volume
 
*Output Volume
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**Set the output image file which the filters should place the final result
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**Pick the output to the algorithm (a label image)
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Tissue Segmentation Parameters:
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*Separate one tissue?
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**Select one tissue type desired to be passed as the output. If checked, the tissue type in Tissue Type option is used
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*Tissue Type
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**Tissue type that will be resulted from the brain segmentation
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Noise Attenuation Parameters:
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*Condutance
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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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*Number Of Iterations
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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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*Q Value
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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
  
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Diffusion Parameters:
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Label Refinement Parameters
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*Conductance
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*Gaussian Sigma
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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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**Label smoothing by a gaussian distribution with variance sigma. The units here is given in mm
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*Number of Iteractions
 
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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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*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}}
 
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* paper
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N/A
  
 
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Latest revision as of 19:24, 30 September 2017

Home < Documentation < Nightly < Modules < BrainStructuresSegmenter


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

BrainStructuresSegmenter-icon.png

This module offers a general brain segmentation pipeline, calling different algorithms depending on the tissue type that the user wants to segment. The list below illustrates the segmentation methods available at moment:

  • Basic Brain Tissues: A general brain tissues segmentation focused on the white matter, gray matter and CSF brain tissue segmentation.
  • Brain Logistic Classification (BLS): A recent brain segmentation method focused on global tissue segmentation, usually CSF, gray and white matter delineations. general brain tissues segmentation focused on the white matter, gray matter and CSF brain tissues segmentation.[1]

NOTE: Each segmentation method listed are related to the segmentation part of the image processing pipeline controlled by this module, i.e. the Brain Structure Segmenter module apply a set of image processing algorithms in which, in the Segmentation part, uses the above methods. The user still has the flexibility to choose what pre and post processing that should be applied in the general image processing pipeline.

The diagram below illustrates the image processing pipeline used in this module

Segmentation pipeline applied internally in this module


Use Cases

  • Use Case 1: Tissue classification
    • There are several image quantitative approaches that use only a certain tissue type (for instance, cortical thickness) in which a previous brain segmentation could be needed.
    • A simple brain tissue mask could be obtained from this module (WM, GM and CSF are available at moment).

  • 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
    • Pick the input to the algorithm. This should be an MRI strutural images with a type listed in the Image Modality option
  • Image Modality
    • MRI strutural image inserted as a input volume
  • Is brain extracted?
    • Is the input data already brain extracted? If not, the ROBEX brain extraction method is used
  • Output Volume
    • Pick the output to the algorithm (a label image)

Tissue Segmentation Parameters:

  • Separate one tissue?
    • Select one tissue type desired to be passed as the output. If checked, the tissue type in Tissue Type option is used
  • Tissue Type
    • Tissue type that will be resulted from the brain segmentation

Noise Attenuation Parameters:

  • Condutance
    • 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
  • Number Of Iterations
    • 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
  • Q Value
    • The anomalous parameter (or q value) is the generalization parameters responsible to give the anomalous process approach on the diffusion equation

Label Refinement Parameters

  • Gaussian Sigma
    • Label smoothing by a gaussian distribution with variance sigma. The units here is given in mm

Similar Modules

References

N/A

Information for Developers

Work in progress
 Section under construction.
  1. ↑ BLS paper
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