UpdateBot: Nightly -> 4.6

2016-11-07T07:42:44Z

Nightly -> 4.6

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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>
Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)<br>
Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email><br>
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|Image:CSIM-logo.png|CSIM Laboratory
|Image:USP-logo.png|University of Sao Paulo
|Image:CNPq-logo.png|CNPq Brazil
|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.

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.


{{documentation/{{documentation/version}}/module-section|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 classication.
* 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.

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Image:AllHeadMRI.png|T1w MRI
Image:BrainMRI.png|Brain extracted T1 MRI
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{{documentation/{{documentation/version}}/module-section|Tutorials}}
N/A


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[[Image:brainextractiontool_gui.png|thumb|380px|User Interface]]
*Input Volume:
**Insert an input scalar volume
*Output Volume:
**Set the output volume
*Binary Mask:
**Set the output binary mask, i.e. the label generated by the brain given in the output volume.


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/{{documentation/version}}/Modules/SkullStripper|Skull Stripper]]
*[[Documentation/{{documentation/version}}/Modules/SwissSkullStripper|Swiss Skull Stripper]]


{{documentation/{{documentation/version}}/module-section|References}}
* [http://dcm.ffclrp.usp.br/csim Computing in Signals and Images in Medicine Laboratory (CSIM)]

Repositories:
* Source code: [https://github.com/CSIM-Toolkits/Slicer GitHub repository]
* Issue tracker: [https://github.com/CSIM-Toolkits/Slicer/issues open issues and enhancement requests]


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[[Category:Documentation/{{documentation/version}}/Modules/Informatics]]

Documentation/4.6/Modules/BrainExtractionTool - Revision history

UpdateBot: Nightly -> 4.6