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

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{{documentation/{{documentation/version}}/module-description}}
 
{{documentation/{{documentation/version}}/module-description}}
 
[[Image:ROBEXBrainExtraction-logo.png|left]]
 
[[Image:ROBEXBrainExtraction-logo.png|left]]
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This module aims to extracted the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper] and also the [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction module repository] in order to see the licenses applied in this project.
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This module aims to extract the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper] and also the [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction module repository] in order to see the licenses applied in this project.
  
 
In addition, for better brain volume extraction results it could be applied some preprocessing steps such as a noise attenuation and field inhomogeneity correction methods in advance. See the modules [[Documentation/{{documentation/version}}/Modules/AADImageFilter|AAD Image Filter]] and [[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection|N4 Bias Field Correction]].
 
In addition, for better brain volume extraction results it could be applied some preprocessing steps such as a noise attenuation and field inhomogeneity correction methods in advance. See the modules [[Documentation/{{documentation/version}}/Modules/AADImageFilter|AAD Image Filter]] and [[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection|N4 Bias Field Correction]].
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**Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation as well as the global brain atrophy.
 
**Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation as well as the global brain atrophy.
  
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<gallery>
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<gallery widths="300px" heights="300px" perrow="3">
 
Image:AllHeadMRI.png|T1w MRI
 
Image:AllHeadMRI.png|T1w MRI
 
Image:BrainMRI.png|Brain extracted T1 MRI
 
Image:BrainMRI.png|Brain extracted T1 MRI
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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:ROBEXBrainExtraction-GUI.png|thumb|482px|User Interface]]
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[[Image:ROBEXBrainExtraction-GUI.png|thumb|300px|User Interface]]
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*Input Volume:
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*'''Input Volume:'''
 
**Insert an input scalar volume
 
**Insert an input scalar volume
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*Output Volume:
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*'''Output Volume:'''
 
**Set the output volume
 
**Set the output volume
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*'''Brain Mask:'''
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**Set, if desired, the output binary mask resulted from the skull stripping process.
  
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'''NOTE''': The ROBEX brain extraction method is fully automatic and does not requires any input value.
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'''NOTE''': The ROBEX brain extraction method is fully automatic and does not require any input parameter.
  
 
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{{documentation/{{documentation/version}}/module-section|References}}
 
{{documentation/{{documentation/version}}/module-section|References}}
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* [http://dcm.ffclrp.usp.br/csim Computing in Signals and Images in Medicine Laboratory (CSIM)]
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* Iglesias JE, Liu CY, Thompson P, Tu Z: "Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods", IEEE Transactions on Medical Imaging, 30(9), 2011, 1617-1634.
  
 
Repositories:
 
Repositories:
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* Official ROBEX repository: https://www.nitrc.org/projects/robex
 
* Source code: [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction GitHub repository]
 
* Source code: [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction GitHub repository]
 
* Issue tracker:  [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction/issues open issues and enhancement requests]
 
* Issue tracker:  [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction/issues open issues and enhancement requests]

Latest revision as of 13:07, 25 July 2017

Home < Documentation < Nightly < Modules < ROBEXBrainExtraction


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Introduction and Acknowledgements

This work was partially funded by CAPES and CNPq (grant 201871/2015-7/SWE), a Brazilian research support agency. Information on CAPES can be obtained from the official websites, CAPES here and CAPES here.
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  
CNPq Brazil  
CAPES Brazil  

Module Description

ROBEXBrainExtraction-logo.png

This module aims to extract the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. [1]. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the original paper and also the module repository in order to see the licenses applied in this project.

In addition, for better brain volume extraction results it could be applied some preprocessing steps such as a noise attenuation and field inhomogeneity correction methods in advance. See the modules AAD Image Filter and N4 Bias Field Correction.

Use Cases

Most frequently used for these scenarios:

  • Use Case 1: Initial image processing step for many neuroimage analysis, such as in DTI and cortical thickness estimation.
    • It would be useful for tissue classification.
  • Use Case 2: Information reduction to registration pipelines
    • The non-brain tissues tend to decrease the quality of many registration algorithms.
  • Use Case 3: Increase the precision of classification algorithms for neurodegenerative brain diseases diagnosis.
    • Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation as well as the global brain atrophy.

  • T1w MRI

  • Brain extracted T1 MRI


Tutorials

N/A

Panels and their use

User Interface
  • Input Volume:
    • Insert an input scalar volume
  • Output Volume:
    • Set the output volume
  • Brain Mask:
    • Set, if desired, the output binary mask resulted from the skull stripping process.

NOTE: The ROBEX brain extraction method is fully automatic and does not require any input parameter.

Similar Modules

References

  • Iglesias JE, Liu CY, Thompson P, Tu Z: "Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods", IEEE Transactions on Medical Imaging, 30(9), 2011, 1617-1634.

Repositories:

  1. ↑ Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634
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