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| − | {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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| − | {{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}} }}
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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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| − | {{documentation/{{documentation/version}}/module-introduction-row}}
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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-introduction-end}}
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| − | {{documentation/{{documentation/version}}/module-section|Module Description}}
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| − | {{documentation/{{documentation/version}}/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, better results could be achieved if some pre processing steps are performed before calling the brain extraction module. I suggest the use of a noise filtering method 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]] as some examples of useful modules for those image pre-processing steps.
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| − | {{documentation/{{documentation/version}}/module-section|Use Cases}}
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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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| − | </gallery>
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| − | {{documentation/{{documentation/version}}/module-section|Tutorials}}
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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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| − | {{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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| − | {{documentation/{{documentation/version}}/module-footer}}
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| − | [[Category:Documentation/{{documentation/version}}/Modules/Informatics]]
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