Difference between revisions of "Documentation/Nightly/RegistrationVideoTutorials"

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Below you will find brief videos/screencasts showing step-by-step approaches to the most common tasks. Most have no audio.
 
Below you will find brief videos/screencasts showing step-by-step approaches to the most common tasks. Most have no audio.
 
== Registration Masking: How do I quickly generate a mask for use in registration? ==
 
== Registration Masking: How do I quickly generate a mask for use in registration? ==
There are several segmentation and editor tools that you can use to quickly generate a map to focus a registration onto the important content. Six approaches are shown below, but not all work equally well for all images. We recommend you try a few on your data to get a feel of what works best for your needs. Also remember that masks for registration differ from other segmentation in that they do not need to be highly accurate.
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There are several segmentation and editor tools that you can use to quickly generate a map to focus a registration onto the important content. Six approaches are shown below, but not all work equally well for all images. We show these to give an introduction and overview over the tools available and the general approach in using them. Many variations and combinations exist. We therefore recommend you try a few on your data to get a feel of what works best for your needs. Also remember that masks for registration differ from other segmentation in that they do not need to be highly accurate. W
 
#[[Media:QuickMask_00_subsample.mov|'''subsample input image''']]: One effective way to reduce manual editing time is to create a subsampled version of your data before you start, perform the segmentation/editing on the subsampled mask and then upsample again in the end. This works best for masks of structures that are large and have relatively smooth surfaces.
 
#[[Media:QuickMask_00_subsample.mov|'''subsample input image''']]: One effective way to reduce manual editing time is to create a subsampled version of your data before you start, perform the segmentation/editing on the subsampled mask and then upsample again in the end. This works best for masks of structures that are large and have relatively smooth surfaces.
 
#[[Media:QuickMask_01_Otsu.mov|'''Otsu automated thresholding''']]: This fast and simple method works well if you seek to separate an object from the background. This will require some post-editing to cleanup. Not recommended for small structures.
 
#[[Media:QuickMask_01_Otsu.mov|'''Otsu automated thresholding''']]: This fast and simple method works well if you seek to separate an object from the background. This will require some post-editing to cleanup. Not recommended for small structures.
 
#[[Media:QuickMask_02_RobustStats.mov|'''Robust Statistics Segmenter''']]: This is a very effective segmenter that requires an initial "rough" segmentation as a starting point. You control the result via parameters for the smoothness, size and expected intensity variation of your object. Quite efficient in combination with level sets, as shown in the video.
 
#[[Media:QuickMask_02_RobustStats.mov|'''Robust Statistics Segmenter''']]: This is a very effective segmenter that requires an initial "rough" segmentation as a starting point. You control the result via parameters for the smoothness, size and expected intensity variation of your object. Quite efficient in combination with level sets, as shown in the video.
 
#[[Media:QuickMask_03_GrowCut.mov|'''Grow-Cut Segmenter''']]: This is a very powerful algorithm with sparse controls. Fast and effective.  It also requires an initial "rough" segmentation as a starting point. You provide sample regions of at least 2 structures and let the module extrapolate. You can then reiterate by applying manual corrections and rerun. Very effective in combination with level sets, as shown in the video.
 
#[[Media:QuickMask_03_GrowCut.mov|'''Grow-Cut Segmenter''']]: This is a very powerful algorithm with sparse controls. Fast and effective.  It also requires an initial "rough" segmentation as a starting point. You provide sample regions of at least 2 structures and let the module extrapolate. You can then reiterate by applying manual corrections and rerun. Very effective in combination with level sets, as shown in the video.
 +
#[[Media:QuickMask_04_ThresholdPaintbrush.mov|'''Threshold Paintbrush''']]: This editor tools combines the standard paintbrush with a threshold effect. You paint your ROI but only pixels within a specified threshold are selected. This is very useful to trace complex edges, which you can pick up effectively with a large brush in a few strokes.
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#[[Media:QuickMask_05_GlobalThreshold.mov|'''Global Threshold''']]: This editor tool is the standard global threshold applied to the entire image. This simple approach can be very efficient if your structure of interest has good contrast and separates well spatially. Morphological filtering can be applied to clean up the result.
 +
#[[Media:QuickMask_06_RegionGrowing.mov|'''Region Growing''']]: This tool requires you to select a few landmarks/fiducial points inside your object and will then try to grow outward looking for edges. Works best if your object has strong edges and is not connected to neighboring structures.  It's fast enough for you to quickly try multiple parameter settings, but results will vary. Often one of the above tools will be more effective.

Revision as of 21:01, 7 May 2013

Home < Documentation < Nightly < RegistrationVideoTutorials

Slicer Registration Training Videos

Below you will find brief videos/screencasts showing step-by-step approaches to the most common tasks. Most have no audio.

Registration Masking: How do I quickly generate a mask for use in registration?

There are several segmentation and editor tools that you can use to quickly generate a map to focus a registration onto the important content. Six approaches are shown below, but not all work equally well for all images. We show these to give an introduction and overview over the tools available and the general approach in using them. Many variations and combinations exist. We therefore recommend you try a few on your data to get a feel of what works best for your needs. Also remember that masks for registration differ from other segmentation in that they do not need to be highly accurate. W

  1. subsample input image: One effective way to reduce manual editing time is to create a subsampled version of your data before you start, perform the segmentation/editing on the subsampled mask and then upsample again in the end. This works best for masks of structures that are large and have relatively smooth surfaces.
  2. Otsu automated thresholding: This fast and simple method works well if you seek to separate an object from the background. This will require some post-editing to cleanup. Not recommended for small structures.
  3. Robust Statistics Segmenter: This is a very effective segmenter that requires an initial "rough" segmentation as a starting point. You control the result via parameters for the smoothness, size and expected intensity variation of your object. Quite efficient in combination with level sets, as shown in the video.
  4. Grow-Cut Segmenter: This is a very powerful algorithm with sparse controls. Fast and effective. It also requires an initial "rough" segmentation as a starting point. You provide sample regions of at least 2 structures and let the module extrapolate. You can then reiterate by applying manual corrections and rerun. Very effective in combination with level sets, as shown in the video.
  5. Threshold Paintbrush: This editor tools combines the standard paintbrush with a threshold effect. You paint your ROI but only pixels within a specified threshold are selected. This is very useful to trace complex edges, which you can pick up effectively with a large brush in a few strokes.
  6. Global Threshold: This editor tool is the standard global threshold applied to the entire image. This simple approach can be very efficient if your structure of interest has good contrast and separates well spatially. Morphological filtering can be applied to clean up the result.
  7. Region Growing: This tool requires you to select a few landmarks/fiducial points inside your object and will then try to grow outward looking for edges. Works best if your object has strong edges and is not connected to neighboring structures. It's fast enough for you to quickly try multiple parameter settings, but results will vary. Often one of the above tools will be more effective.