Difference between revisions of "Documentation/Nightly/Extensions/IASEM"

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| image = [[File:InProgress.png|40px|alt=Work in progress]]
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| text  = WARNING: This module is Work in Progress, which means:
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* the functionality provided by this module may change drastically in the future releases of 3D Slicer
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* the scenes containing data types specific to this module may not be readable by the future versions of Slicer
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* the functionality and user interface may change at any time
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* some functionality may not work as intended
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* testing and documentation is limited
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Acknowledgments:<br>
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Acknowledgments:
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This work is supported by NLM, and the Slicer Community. <br>
 
Author: Bradley Lowekamp<br>
 
Author: Bradley Lowekamp<br>
 
Contact: Bradley Lowekamp <email>blowekamp@mail.nih.gov</email><br>
 
Contact: Bradley Lowekamp <email>blowekamp@mail.nih.gov</email><br>
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License:
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License: [http://raw.github.com/blowekamp/Slicer-IASEM/master/LICENSE Apache License 2.0]
 
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|{{collaborator|logo|nlm}}|{{collaborator|longname|nlm}}
 
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{{documentation/{{documentation/version}}/extension-section|Extension Description}}
 
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Plastimatch is an open source software for image computation. Our main focus is high-performance volumetric registration of medical images, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Software features include:
 
  
−
*B-spline method for deformable image registration (GPU and multicore accelerated)
+
This extension is a compilation of modules which are useful for segmentation and processing of IASEM Electron Microscopy images. It adds modules which help in general with segmenting large data sets, and has components to deal with images in multi-resolutions.
−
*Demons method for deformable image registration (GPU accelerated)
+
 
−
*ITK-based algorithms for translation, rigid, affine, demons, and B-spline registration
+
IASEM stands for ion-abrasion scanning electron microscopy, some times also call dual-beam or focused ion-beam scanning electron microscopy (FIBSEM).
−
*Pipelined, multi-stage registration framework with seamless conversion between most algorithms and transform types
+
 
−
*Landmark-based deformable registration using thin-plate splines for global registration
+
Sample data can be downloaded from http://placid.nlm.nih.gov/community/22, in particular the dateset below is the [http://placid.nlm.nih.gov/community/22 GoldBeads].
−
*Landmark-based deformable registration using radial basis functions for local corrections
+
 
−
*Broad support for 3D image file formats (using ITK), including DICOM, Nifti, NRRD, MetaImage, and Analyze
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<gallery widths="200px" perrow="4">
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*DICOM and DICOM-RT import and export
+
Image:IASEM-Screenshot1.png|Segmentation of Gold particles.
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*XiO import and export
+
</gallery>
  
 
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{{documentation/{{documentation/version}}/extension-section|Modules}}
 
{{documentation/{{documentation/version}}/extension-section|Modules}}
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*[[Documentation/{{documentation/version}}/Modules/PlmBSplineDeformableRegistration|Plastimatch Automatic deformable image registration]]
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*[[Documentation/{{documentation/version}}/Modules/PlmDICOMRTImport|Plastimatch DICOM-RT import]]
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* BinShrink Image Filter
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*[[Documentation/{{documentation/version}}/Modules/PlmLANDWARP|Plastimatch LANDWARP Landmark]]
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* SpacingAwareCurvatureDiffusion Image Filter
 +
* IASEM Import
 +
* IASEM Series Import
 +
* Label Object Statistics
 +
* DoubleThreshold Editor Effect
 +
* BinaryWatershed Editor Effect
 +
 
  
 
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{{documentation/{{documentation/version}}/extension-section|Use Cases}}
 
{{documentation/{{documentation/version}}/extension-section|Use Cases}}
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[http://plastimatch.org/data_sources.html Sample data] to use with modules.
+
===BinShrink Image Filter===
−
<gallery widths="200px" perrow="4">
+
Bin Shrink is one of the modules used to help ease the process of segmentation. By stipulating a neighborhood size, 5x5x5 for example, it will average the neighboring pixels within that 5x5x5 region and turn them into one pixel. Applying the Bin Shrink will result in reduced resolution and a less noisy image. Based on the features you want to segment, you can apply a more aggressive bin or a less aggressive bin based on the complexity of your features of interest. Binning the image does not significantly affect the quality of the image, it also reduces the amount of memory the computer needs to process the data, so the more an image is binned the faster slicer will work and the more free memory you will have.
−
Image:plastimatch_dicomrt_ss.png|DICOM-RT Structure Set
+
 
−
Image:plastimatch_dicomrt_dose.png|DICOM-RT Dose
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−
Image:plastimatch_image_1.png|Before Registration
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{|
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Image:plastimatch_image_2.png|After Registration
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|[[Image:GoldGrayscale.png|thumb|Original gray scale image.]]
−
Image:before_reg.png|Before Registration
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|[[Image:GoldGrayscaleBin5.png|thumb|After running the BinShrink by a factor of 5x5x5.]]
−
Image:after_reg.png|After Registration
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|}
−
</gallery>
+
 
 +
===SpacingAwareCurvatureDiffusion Image Filter===
 +
===IASEM Import===
 +
IASEM Import performs streaming image file reading while reducing the resolution and extracting a region of interest.
 +
 
 +
When dealing with large microscopy image files, the whole volume can be loaded at low resolutions by specifying "Shrink Factors" of "10,10,10". This will still read the entire volume but perform averaging to reduce the resolution. However if "Disable Z-Averaging" is selected not averaging will be performed in the Z-direction, and the amount of data read will be reduced.
 +
 
 +
After an area of interest is visually  determine the ROI tool can be used to define a region of interest, which can be specified as the "Image ROI" and a sub-volume can be loaded at a higher resolution.
 +
 
 +
===IASEM Series Import===
 +
This module imports a series of TIFF images and optionally applies a slice by slice transform. It support the same multi-resolution and region of interest features as the IASEM Import module.
 +
===Label Object Statistics===
 +
===DoubleThreshold Editor Effect===
 +
===BinaryWatershed Editor Effect===
 +
 
 +
<!-- ---------------------------- -->
 +
{{documentation/{{documentation/version}}/extension-section|Walk Through}}
 +
===Multi-Resolution Label Map Segmentation===
 +
When a data set is too large we can use a combination of tools and modules to help speed things up. We can BinShrink the gray scale to reduce resolution and size, and we can crop the region of interest. Cropping, however distorts the dimensions of the volume and we must resample it to match the original gray scale when putting them back together.
 +
{|
 +
|[[Image:OriginalGoldGrayscale.png|thumb|Step 1: Original gray scale image.]]
 +
|[[Image:BinShrinkSettings.png|thumb|Step 2: BinShrink settings.]]
 +
|[[Image:BinShrinkGoldGrayscale.png|thumb|Step 3: After running the BinShrink by a factor of 5x5x5.]]
 +
|[[Image:ROIToolbar.png|thumb|Step 4: ROI Toolbar.]]
 +
|[[Image:CropGoldGrayscale.png|thumb|Step 5: Cropped gray scale image.]]
 +
|[[Image:ThresholdGoldLabelMap.png|thumb|Step 6: Threshold of the cropped gray scale image.]]
 +
|}
 +
#[[Documentation/{{documentation/version}}/Modules/SlicerWelcome#LoadPanel|Load]] gray scale data.
 +
#Go to the Modules search bar, search and select "BinShrink". Go to the BinShrink settings, change the input to the original gray scale image and change the output to "create a new volume".
 +
#Apply the BinShrink Image Filter.
 +
#Create a Region of Interest (ROI) using the Fiducial Module in the Slicer Toolbar.
 +
#Use the [[Documentation/{{documentation/version}}/Modules/CropVolume|Crop Volume]] module to crop the region of interest. '''Make sure to check the box for Voxel based cropping, since this is a gray scale and not a label map.'''
 +
#Apply  [[Documentation/{{documentation/version}}/Modules/Editor#Threshold|Threshold Effect]], or any other editing technique, to your gray scale image to create a label map.
 +
#[[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample]] the label map so its dimensions match those of the gray scale image.
 +
##Using the [[Documentation/{{documentation/version}}/Modules/Transforms|Transforms]] module, create a new transform and rename it "Identity".
 +
##Search and select "Resample Image (BRAINS)" in the modules search bar. Go to the [[Documentation/{{documentation/version}}/Modules/BRAINSResample|Resample]] settings to resample the label map.
 +
##The Image to Warp should be the label map.
 +
##The Reference Image should be the BinShrink gray scale image.
 +
##Create a new Output Image.
 +
##Change the pixel type to "short".
 +
##Change the Transform file to "Identity".
 +
##Change the Interpolation Mode to "NearestNeighbor".
 +
##Apply the Resampling.
 +
*'''NearestNeighbor must be used when resampling label maps.'''
 +
{|
 +
|[[Image:TransformModule.png|thumb|Transform Settings: Creating Active Transform "identity"]]
 +
|[[Image:ResampleSettings.png|thumb|Resample settings.]]
 +
|}
  
 
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{{documentation/{{documentation/version}}/extension-section|Tutorials}}
 
{{documentation/{{documentation/version}}/extension-section|Tutorials}}
−
 
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<gallery widths="200px" perrow="4">
 
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Image:plastimatch_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1023/3D_Slicer_Plastimatch_Registration_Tutorial.ppt Download tutorial]
 
−
Image:plastimatch_tutorial_data.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/85/1004/rider-lung-images.tar.gz Download tutorial data]
 
−
Image:plastimatch_dicomrt_tutorial_ppt.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1568/3D_Slicer_Plastimatch_DICOM_RT_Tutorial_2010_12_28.pdf Download tutorial (PDF)]
 
−
Image:plastimatch_dicomrt_ss.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/85/934/chest-phantom-dicomrt-xio-4.33.02.tar.gz (Download tutorial data)]
 
−
Image:3D_Slicer_Plastimatch_Landmark_Registration_Tutorial.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/110/1622/3D_Slicer_Plastimatch_Landmark_Registration_Tutorial.ppt Download tutorial]
 
−
Image:fixed.png|[http://forge.abcd.harvard.edu/gf/download/frsrelease/85/1619/synthetic_img.zip Download tutorial data]
 
−
</gallery>
 
  
 
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{{documentation/{{documentation/version}}/extension-section|Similar Extensions}}
 
{{documentation/{{documentation/version}}/extension-section|Similar Extensions}}
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N/A
 
  
 
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{{documentation/{{documentation/version}}/extension-section|References}}
 
{{documentation/{{documentation/version}}/extension-section|References}}
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* G Sharp, N Kandasamy, H Singh, M Folkert, "GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration," Physics in Medicine and Biology, 52(19), pp 5771-83, 2007.
 
−
* V Boldea, G Sharp, SB Jiang, D Sarrut, "4D-CT lung motion estimation with deformable registration: Quantification of motion nonlinearity and hysteresis," Medical Physics, 33(3), pp 1008-18, 2008.
 
−
* Z Wu, E Rietzel, V Boldea, D Sarrut, G Sharp, "Evaluation of deformable registration of patient lung 4DCT with sub-anatomical region segmentations," Medical Physics, 35(2), pp 775-81, 2008.
 
−
* G Sharp et al. "Plastimatch - An open source software suite for radiotherapy image processing," Proceedings of the XVIth International Conference on the use of Computers in Radiotherapy, May, 2010.
 
−
* N. Shusharina, G. Sharp "Landmark-based image registration with analytic regularization", IEEE Trans. Med. Imag., submitted, 2011.
 
  
 
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Latest revision as of 16:55, 6 November 2014

Home < Documentation < Nightly < Extensions < IASEM


For the latest Slicer documentation, visit the read-the-docs.


Work in progress
 WARNING: This module is Work in Progress, which means:
  • the functionality provided by this module may change drastically in the future releases of 3D Slicer
  • the scenes containing data types specific to this module may not be readable by the future versions of Slicer
  • the functionality and user interface may change at any time
  • some functionality may not work as intended
  • testing and documentation is limited

Introduction and Acknowledgements

Acknowledgments: This work is supported by NLM, and the Slicer Community.
Author: Bradley Lowekamp
Contact: Bradley Lowekamp <email>blowekamp@mail.nih.gov</email>
License: Apache License 2.0

The National Library of Medicine  
National Alliance for Medical Image Computing (NA-MIC)  


Extension Description

This extension is a compilation of modules which are useful for segmentation and processing of IASEM Electron Microscopy images. It adds modules which help in general with segmenting large data sets, and has components to deal with images in multi-resolutions.

IASEM stands for ion-abrasion scanning electron microscopy, some times also call dual-beam or focused ion-beam scanning electron microscopy (FIBSEM).

Sample data can be downloaded from http://placid.nlm.nih.gov/community/22, in particular the dateset below is the GoldBeads.

  • Segmentation of Gold particles.

Modules

  • BinShrink Image Filter
  • SpacingAwareCurvatureDiffusion Image Filter
  • IASEM Import
  • IASEM Series Import
  • Label Object Statistics
  • DoubleThreshold Editor Effect
  • BinaryWatershed Editor Effect


Use Cases

BinShrink Image Filter

Bin Shrink is one of the modules used to help ease the process of segmentation. By stipulating a neighborhood size, 5x5x5 for example, it will average the neighboring pixels within that 5x5x5 region and turn them into one pixel. Applying the Bin Shrink will result in reduced resolution and a less noisy image. Based on the features you want to segment, you can apply a more aggressive bin or a less aggressive bin based on the complexity of your features of interest. Binning the image does not significantly affect the quality of the image, it also reduces the amount of memory the computer needs to process the data, so the more an image is binned the faster slicer will work and the more free memory you will have.


Original gray scale image.
After running the BinShrink by a factor of 5x5x5.

SpacingAwareCurvatureDiffusion Image Filter

IASEM Import

IASEM Import performs streaming image file reading while reducing the resolution and extracting a region of interest.

When dealing with large microscopy image files, the whole volume can be loaded at low resolutions by specifying "Shrink Factors" of "10,10,10". This will still read the entire volume but perform averaging to reduce the resolution. However if "Disable Z-Averaging" is selected not averaging will be performed in the Z-direction, and the amount of data read will be reduced.

After an area of interest is visually determine the ROI tool can be used to define a region of interest, which can be specified as the "Image ROI" and a sub-volume can be loaded at a higher resolution.

IASEM Series Import

This module imports a series of TIFF images and optionally applies a slice by slice transform. It support the same multi-resolution and region of interest features as the IASEM Import module.

Label Object Statistics

DoubleThreshold Editor Effect

BinaryWatershed Editor Effect

Walk Through

Multi-Resolution Label Map Segmentation

When a data set is too large we can use a combination of tools and modules to help speed things up. We can BinShrink the gray scale to reduce resolution and size, and we can crop the region of interest. Cropping, however distorts the dimensions of the volume and we must resample it to match the original gray scale when putting them back together.

Step 1: Original gray scale image.
Step 2: BinShrink settings.
Step 3: After running the BinShrink by a factor of 5x5x5.
Step 4: ROI Toolbar.
Step 5: Cropped gray scale image.
Step 6: Threshold of the cropped gray scale image.
  1. Load gray scale data.
  2. Go to the Modules search bar, search and select "BinShrink". Go to the BinShrink settings, change the input to the original gray scale image and change the output to "create a new volume".
  3. Apply the BinShrink Image Filter.
  4. Create a Region of Interest (ROI) using the Fiducial Module in the Slicer Toolbar.
  5. Use the Crop Volume module to crop the region of interest. Make sure to check the box for Voxel based cropping, since this is a gray scale and not a label map.
  6. Apply Threshold Effect, or any other editing technique, to your gray scale image to create a label map.
  7. Resample the label map so its dimensions match those of the gray scale image.
    1. Using the Transforms module, create a new transform and rename it "Identity".
    2. Search and select "Resample Image (BRAINS)" in the modules search bar. Go to the Resample settings to resample the label map.
    3. The Image to Warp should be the label map.
    4. The Reference Image should be the BinShrink gray scale image.
    5. Create a new Output Image.
    6. Change the pixel type to "short".
    7. Change the Transform file to "Identity".
    8. Change the Interpolation Mode to "NearestNeighbor".
    9. Apply the Resampling.
  • NearestNeighbor must be used when resampling label maps.
Transform Settings: Creating Active Transform "identity"
Resample settings.

Tutorials

Similar Extensions

References

Information for Developers

Work in progress
 Section under construction.
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