Slicer Wiki - User contributions [en]

Documentation/Labs

2019-08-21T14:37:06Z

Wprummel:

This is the place where we will keep track of our experiments and projects.

__TOC__

= On-going =

== Roadmap ==
* [[{{FULLPAGENAME}}/Slicer5-roadmap|Slicer 5]]

== Internals ==
* [[{{FULLPAGENAME}}/NrrdReading_Writing_Optimizations|NrrdReading_Writing_Optimizations]]
* [[{{FULLPAGENAME}}/OpenGLFilters|OpenGLFilters]]
* [[{{FULLPAGENAME}}/DeprecatedModules|DeprecatedModules extension]]
* [[{{FULLPAGENAME}}/FHSCompliantDirectoryStructure|FHS compliant directory structure]]
* [[{{FULLPAGENAME}}/FiberTractMeasurementAndVisualization|Fiber Tract measurement and visualization]]
* [[{{FULLPAGENAME}}/VTKWidgets|VTK Widgets improvements]]
* [[{{FULLPAGENAME}}/CLIInfrastructureCleanupAndRefactoring|CLI infrastructure cleanup and refactoring]]
* [[{{FULLPAGENAME}}/UpgradingCompilerInfrastructure|Upgrading Compiler Infrastructure]]
* [[{{FULLPAGENAME}}/ViewInfrastructureImprovements| View Infrastructure Improvements]]
* [[{{FULLPAGENAME}}/CDash Improvements|CDash Improvements]]
* [[{{FULLPAGENAME}}/SlicerBridge|SlicerBridge]]

* [[{{FULLPAGENAME}}/CI-and-NightlyPackagesGeneration|Continuous Integration and Nightly packages build infrastructure]]
* [[{{FULLPAGENAME}}/ParameterSerializer|Parameter Serializer support for CLIs]]
* [[{{FULLPAGENAME}}/Augmented Reality and Virtual Reality support|Augmented Reality and Virtual Reality support]]
* [[{{FULLPAGENAME}}/Infrastucture Status|Infrastucture Status]]
* [[{{FULLPAGENAME}}/Improving Slicer Packages Download Experience|Improving Slicer Packages Download experience]]
* [[{{FULLPAGENAME}}/Sequences|Sequences]]
* [[{{FULLPAGENAME}}/Improving Markups|Improving Markups]]
* [[{{FULLPAGENAME}}/Surface Toolbox update|Surface Toolbox update]]
* [[{{FULLPAGENAME}}/SampleDataModuleImprovements|Sample Data Module Improvements]]
* [[{{FULLPAGENAME}}/BuildSystem_ImproveCMakeConfigurationTime|BuildSystem: Improve CMake configuration time]]

== Libraries ==
* [[{{FULLPAGENAME}}/VTK-Orientation|Design: Addition of orientation to VTK data structures]]
* [[{{FULLPAGENAME}}/VTK-String|Design: Make VTK strings encoding aware]]

== Python ==
* [[{{FULLPAGENAME}}/CallingPythonMethodsFromCpp|Calling Python methods from Cpp]]
* [[{{FULLPAGENAME}}/IPython|IPython]]
* [[{{FULLPAGENAME}}/PythonCondaBuild|Python conda build]]

== Compilers & IDE ==
* [[{{FULLPAGENAME}}/ModernizeC++|Modernize to c++11 and beyond]]

== Virtual Machines ==
* [[{{FULLPAGENAME}}/GPU Virtualization|GPU Virtualization]]

== Documentation ==
* [[{{FULLPAGENAME}}/DocumentationImprovments|Documentation Improvements (Wiki, website, ...)]]
* [[{{FULLPAGENAME}}/ModulesAndEvents|Intermediate documentation for developers]]

== Tutorials ==
* [[{{FULLPAGENAME}}/IPythonSlicerTutorials|IPython Slicer Tutorials]]

== Source code management ==
* [[{{FULLPAGENAME}}/TransitionToGit|Transition to GitHub as authoritative version control system]]

== Extension ==

* [[{{FULLPAGENAME}}/ExtensionsServer|Extensions Server (also described as Extensions Manager or Catalog)]]
* [[{{FULLPAGENAME}}/ExtensionsFrameworkRoadmap|Extensions Framework Roadmap]]
* [[{{FULLPAGENAME}}/CustomSlicerGenerator|Custom Slicer Generator]]
* [[{{FULLPAGENAME}}/ExtensionsMetadata|Improving Extensions Metadata]]
* [[Slicer_Visualization_module|Brain Connectome Visualization]]

== Functionalities ==
* [[{{FULLPAGENAME}}/FlyThroughNavigation|Fly-through Navigation]]
* [[{{FULLPAGENAME}}/AutomaticUpdateAndInstallationFramework|Automatic Update and Installation Framework]]
* [[{{FULLPAGENAME}}/ApplicationUsageAnalytics|Application usage analytics]]
* [[{{FULLPAGENAME}}/Plotting2DLineSegments|Plotting 2D Line Segments]]

== Packaging ==
* [[{{FULLPAGENAME}}/HomebrewCask|Homebrew Cask]]

= Completed =

* [[Slicer4:Developers|Developer Projects]]

== Extension ==
* [[{{FULLPAGENAME}}/EasyExtensionContribution|Easy Extension Contribution]] - See [[Documentation/Nightly/Developers/ExtensionWizard|ExtensionWizard]]

== Internals ==
* [[{{FULLPAGENAME}}/StartupTimeImprovement|Slicer startup time improvement]]
* [[{{FULLPAGENAME}}/CMake-ified Python|CMake-ified Python]] - See [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21911 r21911], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21912 r21912], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21913 r21913]
* [[{{FULLPAGENAME}}/NonlinearTransforms|Full support for non-linear transforms]]

== Libraries ==
* [[{{FULLPAGENAME}}/Qt5-and-VTK8|Migration to Qt5 and VTK8]]
* [[{{FULLPAGENAME}}/OpenCV|Integration with OpenCV]]
* [[{{FULLPAGENAME}}/ITKv4|ITKv4]]
* [[{{FULLPAGENAME}}/Qt484|Qt484]]
* [[{{FULLPAGENAME}}/VTK6|VTK6]]
* [[{{FULLPAGENAME}}/VTK7|VTK7]]

== Python ==
* [[{{FULLPAGENAME}}/Pip|Pip]]
* [[{{FULLPAGENAME}}/DevelopmentWithGit|Development with Git]] - See [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21863 r21863], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21867 r21867], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21869 r21869], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21879 r21879], [http://viewvc.slicer.org/viewvc.cgi/Slicer4?view=revision&revision=21891 r21891]
* [[{{FULLPAGENAME}}/PythonObserverCallbacks|Python observer callbacks]]

== Compilers & IDE ==
* [[{{FULLPAGENAME}}/Ninja|Ninja]]
* [[{{FULLPAGENAME}}/VS2012|VS2012]]
* [[{{FULLPAGENAME}}/NUMPY171|Support for Numpy 1.7.1]]

== Modules ==
* [[{{FULLPAGENAME}}/SimpleFilters|Simple Filters]]
* [[{{FULLPAGENAME}}/Editor|Editor]]

== Tutorials testing ==
* [[{{FULLPAGENAME}}/TutorialTesting/4.3-Release|4.3 Release]]

== Debug ==
* [[{{FULLPAGENAME}}/BRAINS_and_ITKv4_issue|BRAINS and ITKv4 issue]]

== Internals ==
* [[{{FULLPAGENAME}}/Segmentations|Segmentations]]
* [[{{FULLPAGENAME}}/MultiDimensional Data Management|MultiDimensional Data Management]]
* [[{{FULLPAGENAME}}/DICOMExport|DICOM Export]]
* [[{{FULLPAGENAME}}/SliceViewAnnotations|Slice View Annotations]]
* [[{{FULLPAGENAME}}/SubjectHierarchy|Subject hierarchy module and plugins]]
* [[{{FULLPAGENAME}}/I18N|Internationalization]]
* [[{{FULLPAGENAME}}/Units|Units]]
* [https://github.com/TubeTK/SlicerExecutionModel/wiki/SlicerExecutionModel-Parameter-Serialization SlicerExecutionModel Parameter Serialization]

= Abandoned =

* [[{{FULLPAGENAME}}/SlicerConfigAndUseSlicerTweaks|SlicerConfig and UseSlicer Tweaks]]

Documentation/Labs/Slicer Visualization module

2019-08-21T14:34:39Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices (AAL or Destrieux) with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome, add data cursor and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color

Documentation/Labs/Slicer Visualization module

2019-08-21T14:34:12Z

Wprummel:

[[Documentation/Labs#Extension]]<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices (AAL or Destrieux) with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome, add data cursor and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color

Documentation/Labs/Slicer Visualization module

2019-08-15T15:46:17Z

Wprummel:

Documentation/Labs/Slicer Visualization module

2019-08-15T14:30:30Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices (AAL or Destrieux) with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color

Documentation/Labs/Slicer Visualization module

2019-08-15T14:30:18Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices (AAL or Destrieux)) with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color

Documentation/Labs/Slicer Visualization module

2019-08-15T13:58:25Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color

Documentation/Labs/Slicer Visualization module

2019-08-14T20:53:09Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file (could be binary, but we will not impose any constraints on the range)
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features 3D module:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

'''''Features 2D module:'''''
* Use as much Python as we can (VTK) otherwise C++
* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))

Documentation/Labs/Slicer Visualization module

2019-08-14T20:49:41Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module and then on the 2D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer) or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)
* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices
* Custom graph view representing the associated matrix and define the connection strength by the opacity of the color 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))

Documentation/Labs/Slicer Visualization module

2019-08-14T20:46:05Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D circle visualization of brain connectome
*3D volume visualization of brain connectome
[[File:Civility.png|border|700px]]

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer)or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than the BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)

* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices
* Custom graph view representing the associated matrix 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))

* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

File:Civility.png

2019-08-14T20:30:13Z

Wprummel:

Documentation/Labs/Slicer Visualization module

2019-08-14T20:27:45Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D graph visualization of brain connectome
*3D shape visualization of brain connectome

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer)or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than the BrainNetViewer and CIVILITY (based on Matlab).
* For each node we could sum the connections and generate 2 files. One disk file (how do I save them on a disk?) and one data structure file (how do I store data in memory?)

* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices
* Custom graph view representing the associated matrix 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))

* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

Documentation/Labs/Slicer Visualization module

2019-08-14T20:19:25Z

Wprummel:

<big>'''<big>Overview</big>'''</big>

This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer.

'''''What do we want to visualize?''' ''
Visualizations are based on symmetric and normalized matrices with connectivity values that may be obtained from diffusion connectivity or resting state connectivity studies (region-to-region values). 
The matrices represent a graph, where the edges are the connectivity values and the nodes are brain regions.

'''''What do we mean by connectome?''' ''
The mapping of all neural connections within the brain is what we call a connectome. 
Each connection is defined by its strength (visually it represents the thickness of the connection between two nodes) and this value is found in the matrices (edge files).

<big>'''<big>Goal</big>'''</big> 

Implement two different modules into Slicer:
*2D graph visualization of brain connectome
*3D shape visualization of brain connectome

<big>'''<big>Details</big>'''</big> 

The user will have the choice between the 2D and the 3D visualization module.
First we will focus on the 3D module.

'''''User Inputs (3 ASCII files):'''''
*Volume file
*Node file (and maybe add and option for the user to be able to edit the node characterization ;for example with labels)
*Edge file
The Node file and the Edge file will have to be adaptable formats, that is the reason why we want to use ASCII files.
The Node file should include information about where the node is located. 
The file extension could either be a Landmark Registration format (existing module in Slicer)or a Jason file (including a node description file (x,y,z coordinates and node color index) and a node magnitude file(with just the size of a node)).

'''''Features:'''''
* Python module (using VTK) and maybe C++
* Implement a 3D visualization tool that is faster than the BrainNetViewer and CIVILITY (based on Matlab).
*

* Implement a 2D spherical graph visualization (may apply Hierarchical Edge Bundling), using matrices
* Custom graph view representing the associated matrix 
(possible options: vtkMRMLGraphMarkupNode,vtkSlicerGraphRepresentation3D and associated widget (looking at vtkMRMLMarkupsCurveNode, vtkSlicerCurveRepresentation and vtkSlicerCurveWidget))

* The 3D visualization module should offer the possibility to rotate around the connectome and zoom for more precision.
* Impose a minimum strength on the connectome connections

Documentation/Labs/Slicer Visualization module

2019-08-14T19:32:10Z

Wprummel:

Documentation/Labs/Slicer Visualization module

2019-08-13T14:57:57Z

Wprummel:

Documentation/Labs/Slicer Visualization module

2019-08-09T18:42:58Z

Wprummel:

Documentation/Labs/Slicer Visualization module

2019-08-08T15:25:25Z

Wprummel: Created page with "<big>'''<big>Overview</big>'''</big> This is a page introducing the Slicer Connectome Visualization project for adding new visualization extensions into 3DSlicer. '''''What..."