UpdateBot: Nightly -> 4.10

2018-10-19T00:45:30Z

Nightly -> 4.10

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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Extension: [[Documentation/{{documentation/version}}/Extensions/LesionSpotlight|LesionSpotlight]]<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}}
[[File:AFTSegmenter-icon.png|right]]

This module offers an implementation of a recent Multiple Sclerosis lesion segmentation approach based on a unsupervised method described by Cabezas et al. <ref>Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006</ref>. This module is intended to be used with FLAIR and T1 MRI volumes, which the MS lesions can be detected.


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* Use Case 1: Multiple Sclerosis (MS) lesions segmentation
**Using T1 and FLAIR MRI volumes, it can be possible to detect abnormal voxel signal using a parametric strategy, which delineates white matter signals that does not belongs to the majority neighborhood pattern. More details can be found in the original paper <ref>Cabezas M. et al.(2014) "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding", Computer Methods and Programs in Biomedicine, DOI: 10.1016/j.cmpb.2014.04.006</ref>

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Image:T2FLAIR_patient_AFT.png|Input T2-FLAIR image with Multiple Sclerosis lesions
Image:T2FLAIR_patient_lesionLabel_AFT.png|Lesion map resulted from AFT Segmenter module
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[[Image:aftsegmenter_gui.png|thumb|380px|User Interface]]
'''IO:'''
*'''T1 Volume'''
**Input T1 volume
*'''T2-FLAIR Volume'''
**Input T2-FLAIR volume
*'''Lesion Label'''
**Output a global lesion mask
*'''Is brain extracted?'''
**Is the input data (T1 and T2-FLAIR) already brain extracted?

'''Segmentation Parameters:'''
*'''Absolute Error Threshold'''
**Define the absolute error threshold for gray matter statistics. This measure evaluated the similarity between the MNI152 template and the T2-FLAIR gray matter fluctuation estimative. A higher error gives a higher variability in the final lesion segmentation
*'''Gamma'''
**Define the outlier detection based on units of standard deviation in the T2-FLAIR gray matter voxel intensity distribution
*'''White Matter Matching'''
**Set the local neighborhood searching for label refinement step. This metric defines the percentage of white matter tissue that surrounds the hyperintense lesions. Large values defines a conservative segmentation, i.e. in order to define a true MS lesion, it must be close to certain percentage of white matter area.
*'''Minimum Lesion Size'''
**Set the minimum lesion size adopted as a true lesion in the final lesion map. Units are given in number of voxels
*'''Gray Matter Mask Value'''
**Set the mask value that represents the gray matter. Default is defined based on the ([https://www.slicer.org/wiki/Documentation/Nightly/Extensions/BrainTissuesExtension Basic Brain Tissues] module) output
*'''White Matter Mask Value'''
**Set the mask value that represents the white matter. Default is defined based on the ([https://www.slicer.org/wiki/Documentation/Nightly/Extensions/BrainTissuesExtension Basic Brain Tissues] module) output


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/Nightly/Modules/LSSegmenter|LS Segmenter]]


{{documentation/{{documentation/version}}/module-section|References}}
* Cabezas, M., Oliver, A., Roura, E., Freixenet, J., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À. and Lladó, X. (2014) ‘Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 115(3), pp. 147–161. DOI: 10.1016/j.cmpb.2014.04.006.


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UpdateBot: Nightly -> 4.10