Documentation/Nightly/Modules/BrainStructuresSegmenter

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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 general brain segmentation pipeline, where it call different algorithms depending the tissue type that the user wants to segment. The list below illustrate the methods available at moment:

• Basic Brain Tissues: A general brain tissues segmentation focused on the segmentation task of white matter, gray matter and CSF brain tissues.

Use Cases

• Use Case 1: Tissue classification
  • There are several image quantitative approaches that are applied only in 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

• EM Segmenter

References

N/A