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

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This module offers ...  
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This module offers an implementation of a recent Multiple Sclerosis lesion segmentation approach based on a unsupervised method described by Cabezas et al. <ref>Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006</ref>. This module is intended to be used with FLAIR and T1 MRI volumes, which the MS lesions can be detected.  
  
  
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* Use Case 1: Multiple Sclerosis (MS) lesions segmentation
 
* Use Case 1: Multiple Sclerosis (MS) lesions segmentation
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**...
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**Using T1 and FLAIR MRI volumes, it can be possible to detect abnormal voxel signal using a parametric strategy, which delineates white matter signals that does not belongs to the majority neighborhood pattern. More details can be found in the original paper <ref>Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006</ref>
  
 
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IO:
 
IO:
 
*T1 Volume
 
*T1 Volume
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**Input T1 volume
 
*T2-FLAIR Volume
 
*T2-FLAIR Volume
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**Input T2-FLAIR volume
 
*Lesion Label
 
*Lesion Label
 
**Output a global lesion mask
 
**Output a global lesion mask
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*Is brain extracted?
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Noise Attenuation Parameters:
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**Is the input data (T1 and T2-FLAIR) already brain extracted?
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*Conductance
 
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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. See the reference paper[1] to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images)
 
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Registration Parameters: (based on [https://www.slicer.org/wiki/Documentation/Nightly/Modules/BRAINSFit BRAINSFit] module)
 
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*Percentage Of Samples
 
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**Percentage of voxel used in registration
 
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*Initiation Method
 
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**Initialization method used for the MNI152 registration
 
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*Interpolation
 
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**Choose the interpolation method used to register the standard space to input image space. Options: Linear, NearestNeighbor, B-Spline
 
  
 
Segmentation Parameters:
 
Segmentation Parameters:
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**Define the outlier detection based on units of standard deviation in the T2-FLAIR gray matter voxel intensity distribution
 
**Define the outlier detection based on units of standard deviation in the T2-FLAIR gray matter voxel intensity distribution
 
*White Matter Matching
 
*White Matter Matching
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**Set the local neighborhood searching for label refinement step. This metric defines the percentage of white matter tissue that surrounds the hyperintense lesions. Higher values defines a conservative segmentation
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**Set the local neighborhood searching for label refinement step. This metric defines the percentage of white matter tissue that surrounds the hyperintense lesions. Large values defines a conservative segmentation, i.e. in order to define a true MS lesion, it must be close to certain percentage of white matter area.
 
*Minimum Lesion Size
 
*Minimum Lesion Size
 
**Set the minimum lesion size adopted as a true lesion in the final lesion map. Units are given in number of voxels
 
**Set the minimum lesion size adopted as a true lesion in the final lesion map. Units are given in number of voxels
 
*Gray Matter Mask Value
 
*Gray Matter Mask Value
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**Set the mask value that represents the gray matter. Default is defined based on the Basic Brain Tissues module output
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**Set the mask value that represents the gray matter. Default is defined based on the ([https://www.slicer.org/wiki/Documentation/Nightly/Extensions/BrainTissuesExtension Basic Brain Tissues] extension) output
 
*White Matter Mask Value
 
*White Matter Mask Value
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**Set the mask value that represents the white matter. Default is defined based on the Basic Brain Tissues module output
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**Set the mask value that represents the white matter. Default is defined based on the ([https://www.slicer.org/wiki/Documentation/Nightly/Extensions/BrainTissuesExtension Basic Brain Tissues] extension) output
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
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*[[Documentation/Nightly/Modules/LSSegmenter|LS Segmenter]]
  
 
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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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* Cabezas, M., Oliver, A., Roura, E., Freixenet, J., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À. and Lladó, X. (2014) ‘Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 115(3), pp. 147–161. DOI: 10.1016/j.cmpb.2014.04.006.
  
 
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Revision as of 19:52, 24 July 2017

Home < Documentation < Nightly < Modules < AFTSegmenter


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


Introduction and Acknowledgements

Extension: LesionSpotlight
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

AFTSegmenter-icon.png

This module offers an implementation of a recent Multiple Sclerosis lesion segmentation approach based on a unsupervised method described by Cabezas et al. [1]. This module is intended to be used with FLAIR and T1 MRI volumes, which the MS lesions can be detected.


Use Cases

  • Use Case 1: Multiple Sclerosis (MS) lesions segmentation
    • Using T1 and FLAIR MRI volumes, it can be possible to detect abnormal voxel signal using a parametric strategy, which delineates white matter signals that does not belongs to the majority neighborhood pattern. More details can be found in the original paper [2]

  • Input T2-FLAIR image with Multiple Sclerosis lesions

  • Lesion map resulted from AFT Segmenter module


Panels and their use

User Interface

IO:

  • T1 Volume
    • Input T1 volume
  • T2-FLAIR Volume
    • Input T2-FLAIR volume
  • Lesion Label
    • Output a global lesion mask
  • Is brain extracted?
    • Is the input data (T1 and T2-FLAIR) already brain extracted?

Segmentation Parameters:

  • Absolute Error Threshold
    • Define the absolute error threshold for gray matter statistics. This measure evaluated the similarity between the MNI152 template and the T2-FLAIR gray matter fluctuation estimative. A higher error gives a higher variability in the final lesion segmentation
  • Gamma
    • Define the outlier detection based on units of standard deviation in the T2-FLAIR gray matter voxel intensity distribution
  • White Matter Matching
    • Set the local neighborhood searching for label refinement step. This metric defines the percentage of white matter tissue that surrounds the hyperintense lesions. Large values defines a conservative segmentation, i.e. in order to define a true MS lesion, it must be close to certain percentage of white matter area.
  • Minimum Lesion Size
    • Set the minimum lesion size adopted as a true lesion in the final lesion map. Units are given in number of voxels
  • Gray Matter Mask Value
    • Set the mask value that represents the gray matter. Default is defined based on the (Basic Brain Tissues extension) output
  • White Matter Mask Value
    • Set the mask value that represents the white matter. Default is defined based on the (Basic Brain Tissues extension) output


Similar Modules

References

  • Cabezas, M., Oliver, A., Roura, E., Freixenet, J., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À. and Lladó, X. (2014) ‘Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 115(3), pp. 147–161. DOI: 10.1016/j.cmpb.2014.04.006.

Information for Developers

Work in progress
 Section under construction.
  1. ↑ Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006
  2. ↑ Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006
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