Documentation/Nightly/Modules/BrainLogisticSegmentation - Revision history

Acsenrafilho at 14:46, 24 October 2017

2017-10-24T14:46:49Z

Acsenrafilho: BLS gui description added

2017-10-24T14:18:03Z

BLS gui description added

Acsenrafilho: First version of BLS wiki page

2017-10-24T13:28:03Z

First version of BLS wiki page

New page

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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Extension: [[Documentation/{{documentation/version}}/Extensions/BrainTissuesExtension|Brain Tissues Extension]]<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}}
[[Image:BrainLogisticSegmenter-icon.png|right]]

This CLI module offers the Brain Logistic Segmentation (BLS) algorithm, which is a fully automatic segmentation method for MRI structural images. Although the method was intensively tested over T1 weighted (T1w) MRI images, it could be also applied to similar structural MRI data such as T2w and PDw images. The only requirement that is needed in BLS method is that the image histogram can be enough information to describe each tissue of interest (defined as distinguished peaks at image histogram). Full description of BLS method can be found at paper publish at ... journal (DOI: ).

'''NOTE:''' A small set of pre-processing steps are usually required for a smooth tissue segmentation using BLS. Methods such as image denoising (e.g. [[Documentation/Nightly/Extensions/AnomalousFilters|Anomalous Filters]]) and bias field correction ([[Documentation/Nightly/Modules/N4ITKBiasFieldCorrection|N4 ITK Bias Field Correction]]) can greatly assist to the BLS convergence.


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Use Case 1: General brain tissue segmentation
**There are several image quantitative approaches that use only a certain tissue type (for instance, cortical thickness) in which a previous brain segmentation could be needed.
* Use Case 2: Brain extraction refinement
** In some occasions, the brain extraction procedure may left non-brain tissues on the final brain mask (e.g. dura). Using a simple brain tissue mask offered in BLS method can be helpful to remove undesired non-brian information on further analysis (e.g. cortical thickness, total intracranial volume and brain atrophy).

<gallery widths="300px" heights="300px">
Image:T1_raw_BLS.png| ...
Image:T1_tissues_BLS.png| ...
Image:T1_tissues_GM_3DRender_BLS.png| ...
</gallery>


{{documentation/{{documentation/version}}/module-section|Panels and their use}}

[[Image:BrainLogisticSegmentation_gui.png|thumb|480px|User Interface]]
IO:
*Input Volume
**Pick the input to the algorithm. This should be an MRI strutural images with a type listed in the Image Modality option
*Image Modality
**MRI strutural image inserted as a input volume
*Output Volume
**Pick the output to the algorithm (a label image)

Tissue Segmentation Parameters:
*Separate one tissue?
**Select one tissue type desired to be passed as the output. If checked, the tissue type in Tissue Type option is used
*Tissue Type
**Tissue type that will be resulted from the brain segmentation


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


{{documentation/{{documentation/version}}/module-section|References}}
* paper


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
{{documentation/{{documentation/version}}/module-developerinfo}}


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@@ Line 37: / Line 37: @@
 <gallery widths="300px" heights="300px">
-Image:T1_raw_BLS.png| ...
+Image:T1_raw_BLS.png| T1w MRI image
-Image:T1_tissues_BLS.png| ...
+Image:T1_tissues_BLS.png| T1w MRI brain tissues segmented by BLS method application
-Image:T1_tissues_GM_3DRender_BLS.png| ...
+Image:T1_tissues_GM_3DRender_BLS.png| A gray and white matter 3D representation from the binary segmentation provided by BLS method
 </gallery>

@@ Line 24: / Line 24: @@
 [[Image:BrainLogisticSegmenter-icon.png|right]]
-This CLI module offers the Brain Logistic Segmentation (BLS) algorithm, which is a fully automatic segmentation method for MRI structural images. Although the method was intensively tested over T1 weighted (T1w) MRI images, it could be also applied to similar structural MRI data such as T2w and PDw images. The only requirement that is needed in BLS method is that the image histogram can be enough information to describe each tissue of interest (defined as distinguished peaks at image histogram). Full description of BLS method can be found at paper publish at ... journal (DOI: ).
+This CLI module offers the Brain Logistic Segmentation (BLS) algorithm, which is a fully automatic segmentation method for MRI structural images. Although the method was intensively tested over T1 weighted (T1w) MRI images, it could be also applied to similar structural MRI data such as T2w and PDw images. The only requirement that is needed in BLS method is that the image histogram can be enough information to describe each tissue of interest (defined as distinguished peaks at image histogram). A full description of BLS method can be found at the paper published at ... journal (DOI: ).
 '''NOTE:''' A small set of pre-processing steps are usually required for a smooth tissue segmentation using BLS. Methods such as image denoising (e.g. [[Documentation/Nightly/Extensions/AnomalousFilters|Anomalous Filters]]) and bias field correction ([[Documentation/Nightly/Modules/N4ITKBiasFieldCorrection|N4 ITK Bias Field Correction]]) can greatly assist to the BLS convergence.
@@ Line 34: / Line 34: @@
 **There are several image quantitative approaches that use only a certain tissue type (for instance, cortical thickness) in which a previous brain segmentation could be needed.
 * Use Case 2: Brain extraction refinement
-** In some occasions, the brain extraction procedure may left non-brain tissues on the final brain mask (e.g. dura). Using a simple brain tissue mask offered in BLS method can be helpful to remove undesired non-brian information on further analysis (e.g. cortical thickness, total intracranial volume and brain atrophy).
+** In some occasions, the brain extraction procedure may leave non-brain tissues on the final brain mask (e.g. dura). Using a simple brain tissue mask offered in BLS method can be helpful to remove undesired non-brian information on further analysis (e.g. cortical thickness, total intracranial volume and brain atrophy).
 <gallery widths="300px" heights="300px">
@@ Line 47: / Line 47: @@
 [[Image:BrainLogisticSegmentation_gui.png|thumb|480px|User Interface]]
-IO:
+'''IO''':
-*Input Volume
+*'''Input Volume'''
-**Pick the input to the algorithm. This should be an MRI strutural images with a type listed in the Image Modality option
+** Input volume (T1, T2 or PD MRI images)
-*Image Modality
+*'''Output Tissue'''
-**MRI strutural image inserted as a input volume
+** Output Tissues. A binary mask with all the tissues being represented
-*Output Volume
+* '''Number Of Tissues'''
-**Pick the output to the algorithm (a label image)
+** The number of tissues presented in the input image. This should be driven by the image histogram peaks.
-Tissue Segmentation Parameters:
+'''Advanced Segmentation Parameters''':
-*Separate one tissue?
+* '''Tissue Refinement Threshold'''
-**Select one tissue type desired to be passed as the output. If checked, the tissue type in Tissue Type option is used
+** Defines the logistic classification threshold that should be adopted in order to define the binary mask. If thr=0.5, all the tissues are precisely added without any overlay. If thr<0.5, there is a tissue mask overlay representing the partial volume (PV) among tissues, which is placed in an additional tissue mask (PV masking). If thr>0.5, the overlay is also presented regarding PV, however, the output is changed to zero, i.e. the overlying tissue is not represented as an additional mask.
-*Tissue Type
+* '''Use Manual Number Of Bins'''
-**Tissue type that will be resulted from the brain segmentation
+** Choose if you want to set the number of bins manually.
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