Documentation/Nightly/Modules/LSSegmenter - Revision history

Acsenrafilho at 12:58, 25 July 2017

2017-07-25T12:58:44Z

Acsenrafilho at 20:19, 24 July 2017

2017-07-24T20:19:03Z

Acsenrafilho at 17:57, 7 March 2017

2017-03-07T17:57:58Z

Acsenrafilho at 18:50, 16 February 2017

2017-02-16T18:50:08Z

Acsenrafilho at 15:57, 9 December 2016

2016-12-09T15:57:49Z

Acsenrafilho at 15:55, 9 December 2016

2016-12-09T15:55:52Z

Acsenrafilho at 15:43, 9 December 2016

2016-12-09T15:43:15Z

Acsenrafilho: Created module's wiki page

2016-11-18T19:28:34Z

Created module's 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/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
}}
{{documentation/{{documentation/version}}/module-introduction-end}}


{{documentation/{{documentation/version}}/module-section|Module Description}}
This module offer a simple application ...


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* Use Case 1:
**Something.
<gallery widths="200px" perrow="3">
Image:LSSegmenter_FLAIR.png|Input T2-FLAIR image with multiple sclerosis lesions
Image:LSSegmenter_Label.png|Lesion map resulted from LSSegmenter module
</gallery>


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

[[Image:lssegmenter_gui.png|thumb|380px|User Interface]]
IO:
*Input Volume
**Select the input image
*Output Volume
**Set the output image file which the filters should place the final result

... Parameters:
*Conductance
**The conductance regulates the diffusion intensity in the neighbourhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
* See the reference paper<ref>Da S Senra Filho, A. C., Garrido Salmon, C. E., & Murta Junior, L. O. (2015). Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), 2355–2373. doi:10.1088/0031-9155/60/6/2355</ref> to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).


{{documentation/{{documentation/version}}/module-section|Similar Modules}}
*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]


{{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 24: / Line 24: @@
 [[File:LSSegmenter-icon.png|right]]
-This module offers a hybrid algorithm based on the iterative contrast enhancement (ICE) method and logistic classification for the hyperintense Multiple Sclerosis lesion segmentation task.
+This module offers a hybrid algorithm based on the iterative contrast enhancement (ICE) and logistic classification methods for hyperintense Multiple Sclerosis lesion segmentation task.
@@ Line 42: / Line 42: @@
 [[Image:lssegmenter_gui.png|thumb|380px|User Interface]]
-IO:
+'''IO:'''
 *T1 Volume
-*T2-FLAIR Volume
+*'''T2-FLAIR Volume'''
 *Lesion Label
 **Output a global lesion mask

@@ Line 80: / Line 80: @@
 **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 Basic Brain Tissues module output
+**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 Basic Brain Tissues module output
+**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

@@ Line 24: / Line 24: @@
 [[File:LSSegmenter-icon.png|right]]
-This module offers a hybrid voxel-intensity lesion segmentation method based on logistic classification and local contrast enhancement algorithm for the hyperintense Multiple Sclerosis lesion segmentation task.
+This module offers a hybrid algorithm based on the iterative contrast enhancement (ICE) method and logistic classification for the hyperintense Multiple Sclerosis lesion segmentation task.
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Use Cases}}
-* Use Case 1: Multiple Sclerosis (MS) hyperintense lesions segmentation
+* Use Case 1: Hyperintense Multiple Sclerosis (MS) lesions segmentation
-**Hyperintense MS lesions, mainly in T2-FLAIR images, have a easy application of this segmentations procedure
+**Hyperintense MS lesions, mainly in T2-FLAIR images, have an easy application of this lesion segmentation procedure
 <gallery widths="300px" heights="300px" perrow="3">
@@ Line 77: / Line 78: @@
 **Set the local neighborhood searching for label refinement step. This metric defines the percentage of white matter tissue that surrounds the hyperintense lesions. Higher values defines a conservative segmentation
 *Minimum Lesion Size
-**Set the minimum lesion size adopted as a true lesion in the final lesion map. Units given in number of voxels
+**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 Basic Brain Tissues module output

@@ Line 24: / Line 24: @@
 [[File:LSSegmenter-icon.png|right]]
-This module offer a voxel-intensity lesion segmentation method based on logistic contrast enhancement and threshold level. At moment, this method was studied on hyperintense T2-FLAIR lesion segmentation in Multiple Sclerosis lesion segmentation.
+This module offers a hybrid voxel-intensity lesion segmentation method based on logistic classification and local contrast enhancement algorithm for the hyperintense Multiple Sclerosis lesion segmentation task.
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Use Cases}}
 * Use Case 1: Multiple Sclerosis (MS) hyperintense lesions segmentation
-**Hyperintense Multiple Sclerosis lesions, mainly in T2-FLAIR images, have a easy application of this segmentations procedure
+**Hyperintense MS lesions, mainly in T2-FLAIR images, have a easy application of this segmentations procedure
 <gallery widths="300px" heights="300px" perrow="3">

@@ Line 26: / Line 26: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Use Cases}}
-* Use Case 1:
+* Use Case 1: Multiple Sclerosis (MS) hyperintense lesions segmentation
-**Something.
+**Hyperintense Multiple Sclerosis lesions, mainly in T2-FLAIR images, have a easy application of this segmentations procedure
 <gallery widths="300px" heights="300px" perrow="3">
 Image:T2FLAIR_patient.png|Input T2-FLAIR image with hyperintense Multiple Sclerosis lesions

@@ Line 22: / Line 22: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Module Description}}
-This module offer a simple application ...
+This module offer a voxel-intensity lesion segmentation method based on logistic contrast enhancement and threshold level. At moment, this method was studied on hyperintense T2-FLAIR lesion segmentation in Multiple Sclerosis lesion segmentation.
 <!-- ---------------------------- -->
@@ Line 28: / Line 28: @@
 * Use Case 1:
 **Something.
-<gallery widths="200px" perrow="3">
+<gallery widths="300px" heights="300px" perrow="3">
-Image:LSSegmenter_FLAIR.png|Input T2-FLAIR image with multiple sclerosis lesions
+Image:T2FLAIR_patient.png|Input T2-FLAIR image with hyperintense Multiple Sclerosis lesions
-Image:LSSegmenter_Label.png|Lesion map resulted from LSSegmenter module
+Image:T2FLAIR_patient_lesionLabel.png|Lesion map resulted from LS Segmenter module
 </gallery>
@@ Line 39: / Line 39: @@
 [[Image:lssegmenter_gui.png|thumb|380px|User Interface]]
 IO:
-*Input Volume
+*T1 Volume
-**Select the input image
+*T2-FLAIR Volume
-*Output Volume
+*Lesion Label
-**Set the output image file which the filters should place the final result
+**Output a global lesion mask
-... Parameters:
+Noise Attenuation Parameters:
 *Conductance
 **The conductance regulates the diffusion intensity in the neighbourhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
-* See the reference paper<ref>Da S Senra Filho, A. C., Garrido Salmon, C. E., & Murta Junior, L. O. (2015). Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), 2355–2373. doi:10.1088/0031-9155/60/6/2355</ref> to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).
+*Number Of Iterations
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Similar Modules}}
-*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]
+N/A
 <!-- ---------------------------- -->