UpdateBot: Nightly -> 4.10

2018-10-19T00:45:45Z

Nightly -> 4.10

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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Extension: [[Documentation/{{documentation/version}}/Extensions/BrainTissuesExtension|BrainTissuesExtension]]<br>
Webpage: http://dcm.ffclrp.usp.br/csim/<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:CAPES-logo.png|CAPES Brazil
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{{documentation/{{documentation/version}}/module-section|Module Description}}
This module offer a simple and robust brain tissue segmentation focused on white matter, gray matter and CSF brain tissues. The method applied here is based on naive Bayesian segmentation, which presents good results with high quality T1 weighted MRI images.

'''NOTE''': This module can be used alone (only apply the naive Bayesian segmentation method on the input data), but the [[Documentation/{{documentation/version}}/Modules/BrainStructuresSegmenter|Brain Structures Segmenter]] module already use it internally, which control a image processing pipeline for a better brain tissue segmentation result.


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* Use Case 1: Separate White Matter, Gray Matter and CSF brain tissues from a strucutral MRI image.
**There are some image processing strategies that may need a specific brain tissue mask and this module could facilitate this task. For instance, a Multiple Sclerosis lesion detection algorithm may need a White Matter mask in order to define a localized brain region where the lesion are more probable to appear.
<gallery widths="300px" heights="300px" perrow="3">
Image:T1_tissues.png|White matter, gray matter and CSF tissues segmented from the previous MRI image
Image:WM_3DReconstruction.png|A white matter mask 3D reconstruction
Image:GM_3DReconstruction.png|A gray matter mask 3D reconstruction
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[[Image:basicbraintissues_gui.png|thumb|500px|User Interface]]
IO:
*Input Volume
**Input volume. The algorithm works better with high resolution T1 MRI images alread brain extract and inhomogeneity corrected
*Image Modality
**Select the image modality inserted as a input volume
*Brain Mask
**Output brain tissue mask

Tissue Type Output:
*Separate one tissue class
**Choose if you want all the tissues classes or only one class segmented
*Tissue
**Choose what is the brain tissue label that you want as the output label


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[https://www.slicer.org/wiki/Modules:EMSegmenter-3.6 EM Segmenter]


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N/A


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Documentation/4.10/Modules/BasicBrainTissues - Revision history

UpdateBot: Nightly -> 4.10