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

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
 
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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, [http://www.capes.gov.br/ CAPES here] and [http://www.cnpq.br/ CAPES here].<br>
 
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Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)<br>
 
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Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email><br>
 
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|Image:CSIM-logo.png|CSIM Laboratory
 
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|Image:USP-logo.png|University of Sao Paulo
 
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|Image:CNPq-logo.png|CNPq Brazil
 
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|Image:CAPES-logo.png|CAPES Brazil
 
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{{documentation/{{documentation/version}}/module-section|Module Description}}
 
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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.
 
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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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Most frequently used for these scenarios:
 
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* Use Case 1: Initial image processing step for many neuroimage analysis, such as in DTI and cortical thickness estimation.
 
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**It would be useful for tissue classication.
 
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* Use Case 2: Information reduction to registration pipelines
 
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**The non-brain tissues tend to decrease the quality of many registration algorithms.
 
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* Use Case 3: Increase the precision of classification algorithms for neurodegenerative brain diseases diagnosis.
 
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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.
 
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<gallery>
 
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Image:AllHeadMRI.png|T1w MRI
 
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Image:BrainMRI.png|Brain extracted T1 MRI
 
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N/A
 
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}
 
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[[Image:brainextractiontool_gui.png|thumb|380px|User Interface]]
 
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*Input Volume:
 
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**Insert an input scalar volume
 
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*Output Volume:
 
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**Set the output volume
 
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*Binary Mask:
 
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**Set the output binary mask, i.e. the label generated by the brain given in the output volume.
 
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
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*[[Documentation/{{documentation/version}}/Modules/SkullStripper|Skull Stripper]]
 
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*[[Documentation/{{documentation/version}}/Modules/SwissSkullStripper|Swiss Skull Stripper]]
 
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{{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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Repositories:
 
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* Source code: [https://github.com/CSIM-Toolkits/Slicer GitHub repository]
 
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* Issue tracker:  [https://github.com/CSIM-Toolkits/Slicer/issues open issues and enhancement requests]
 
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[[Category:Documentation/{{documentation/version}}/Modules/Informatics]]
 
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Latest revision as of 12:46, 26 November 2016

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