Difference between revisions of "Documentation/4.3/Extensions/OpenCAD"

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|[[Image:OpenCAD.PNG|thumb|left|200px|OpenCAD]]
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This project is supported by...
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This project is supported by P41 RR019703/RR/NCRR NIH HHS/United States, P01 CA067165/CA/NCI NIH HHS/United States and P41 EB015898/EB/NIBIB NIH HHS/United States
  
 
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{{documentation/{{documentation/version}}/module-section|Module Description}}
 
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{{documentation/modulename}} module allows to output a filtered transform node based on an tracker input (transform node). Tracking sensors are particularly usefull to track surgical tools, robots, patient motion, etc... . However, data imported in 3D Slicer from these devices are often noisy. To reduce the noise and smoothly control objects, such as 3D Slicer camera, the {{documentation/modulename}} module will apply a low-pass filter on input data. The cut-off frequency (i.e. the smoothing parameter) is adjustable by the user.
 
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It is important to notice that filters induce delay, and the smoother the motion is, the bigger the delay between the raw data and the filtered position will be.
 
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{{documentation/{{documentation/version}}/extension-section|Modules}}
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*[[Documentation/{{documentation/version}}/Modules/SegmentCAD|SegmentCAD: Tumor Segmentation from DCE-MRI]]
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*[[Documentation/{{documentation/version}}/Modules/HeterogeneityCAD|HeterogeneityCAD: Analyze Heterogeneity of Tumor Images and their Parameter Maps using various metrics)]]
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{{documentation/{{documentation/version}}/extension-section|Extension Description}}
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*The SegmentCAD module is designed to segment tumors from DCE-MRI datasets which include a pre-contrast image and post-contrast images at different time points.
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**SegmentCAD uses blackbox methods to calculate the wash-in and wash-out slopes from the time-intensity curves.
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**The segmentation output is a Label Map with red, yellow, and blue colors respectively identifying washout (Type III), plateau (Type II), and persistent (Type I) voxels.
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*The HeterogeneityCAD module is an extensible, image feature extraction toolbox primarily to quantify the heterogeneity of tumor images and their label maps.
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**Metrics have been implemented from a variety of feature classes including:
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***First-Order/Histogram statistics
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***Morphology/Shape measures and Geometrical (4D Extrusion) measures
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***Renyi/Fractal dimensions
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***Texture features computed from Gray-Level Co-occurrence Matrices (GLCM) and from Gray-Level Run Length matrices (GLRL)
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{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
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The {{documentation/modulename}} module could be used in any situation requiring stable tracking data and not requiring a "real-time" information. Indeed, applying a filter on raw data will induce a delay (depending on choosen parameters) on the filtered position/orientation.
 
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An example could be to drive Slicer's virtual camera. Having some noise on the transformation driving the camera will cause the camera to "shake" which could be really distracting and uncomfortable to look at. By filtering this raw data, camera motion is smoothed.
 
  
 
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{{documentation/{{documentation/version}}/module-section|Tutorials}}
 
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[[media:OpenCAD.pptx|Tutorial for OpenCAD (pptx)]]‎  
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*SegmentCAD
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**[[Media:SegmentCADTutorial.pptx|SegmentCAD Tutorial (pptx)]]‎
  
 
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{{documentation/{{documentation/version}}/module-section|Data sets}}
 
{{documentation/{{documentation/version}}/module-section|Data sets}}
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[[media:Breast-data.zip|Breast DCE-MRI Data Set 1 (zip file containing the nrrd volumes)]]‎  
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*SegmentCAD
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**[[Media:Breast-data1.zip|Breast DCE-MRI Data Set 1 (zip file containing the nrrd volumes for the  tutorial)]]‎  
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[[media:Breast-data.zip|Breast DCE-MRI Data Set 2 (zip file containing the nrrd volumes)]]‎
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**[[Media:Breast-data2.zip|Breast DCE-MRI Data Set 2 (zip file containing additional test set of nrrd volumes)]]‎
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*HeterogeneityCAD
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**[[Media:BreastHeteroCADData.zip|Breast DCE-MRI Data Set (zip file containing the nrrd volumes for the  tutorial)]]‎  
  
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[[media:Breast-data.zip|Liver DCE-MRI Data Set 1 (zip file containing the nrrd volumes)]]‎
 
  
 
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}
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{{documentation/{{documentation/version}}/module-section|Quick Instructions for Use}}
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{|
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*[[Documentation/{{documentation/version}}/Modules/SegmentCAD|SegmentCAD (Click link for detailed description)]]
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The GUI of the {{documentation/modulename}} module contains 4 elements:
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**Select the pre-contrast volume
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* Input Transform. This is the raw data, usually raw tracker data, represented by a vtkMRMLLinearTransformNode
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**Select the first post-contrast volume
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* Output Transform. This is the filtered transform represented also by a vtkMRMLLinearTransformNode. This node can be created directly from the list
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**Select the second post-contrast volume
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* Filter checkbox. This checkbox turn on/off the filtering
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**Select the third post-contrast volume
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* Smoothing slider. This slider control the smoothing parameter. In the left, smoother, with relatively important delay. In the right, faster (almost no delay), but noisier.
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**Select the fourth post-contrast volume
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**Create or select a label map volume node to represent the output of the segmentation
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{|
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**Click "Apply OpenCAD Segmentation"
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|[[Image:Slicer4-TrackerStabilizer-GUI.png|thumb|400px|{{documentation/modulename}} GUI ]]
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*[[Documentation/{{documentation/version}}/Modules/HeterogeneityCAD|HeterogeneityCAD (Click link for detailed description)]]
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|}
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**Add an image or parameter map (.nrrd file) to the Nodes List
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**Select a corresponding segmentation label map to use as ROI
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**Click "Apply HeterogeneityCAD"
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{{documentation/{{documentation/version}}/module-parametersdescription}}
 
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
{{documentation/{{documentation/version}}/module-section|Similar Modules}}
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N/A
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*SegmentCAD:
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*HeterogeneityCAD:
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**LabelStatistics
  
 
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{{documentation/{{documentation/version}}/module-section|References}}
 
{{documentation/{{documentation/version}}/module-section|References}}
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N/A
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* J. Jayender, E. Gombos, S. Chikarmane, D. Dabydeen, F. A. Jolesz, and K. G. Vosburgh, “Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation”, Journal of Computerized Medical Imaging and Graphics, vol. 37, no. 4, pp. 281-292, 2013
 +
* J. Jayender, S. A. Chikarmane, F. A. Jolesz and E. Gombos, “Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis”, Journal of MRI, Article first published online 23 September 2013, doi: 10.1002/jmri.24394
 +
* J. Jayender, K.G. Vosburgh, E. Gombos, A. Ashraf, D. Kontos, S.C. Gavenonis, F. A. Jolesz and K. Pohl , “Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm”, IEEE International Symposium on Biomedical Imaging, pp. 122-125, 2012.
 +
* J. Jayender, D.T. Ruan, V. Narayan, N. Agrawal, F. A. Jolesz and H. Mamata, “Segmentation of Parathyroid Tumors from DCE-MRI using Linear Dynamic System Analysis”, IEEE International Symposium on Biomedical Imaging, 2013.
 +
* J. Jayender, J. Jagannathan, S.Chikarmane, C.P.Raut and F.A. Jolesz, “Computer-Aided Diagnosis of Breast Angiosarcoma: Results in 14 cases”, Quantitative Medical Imaging Symposium, 2013 (invited paper).
 +
* HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach", vol. 5, Nat Communication, 2014.
 +
 
  
 
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{{documentation/{{documentation/version}}/module-section|Information for Developers}}
 
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{{documentation/{{documentation/version}}/module-developerinfo}}
 
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This module is for now only a low-pass filter, but in the futur, several filters would be implemented to let the choice to the user.
 
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Source code: https://github.com/SlicerIGT/TrackerStabilizer
 
  
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Source code: https://github.com/vnarayan13/Slicer-OpenCAD
 
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Latest revision as of 17:02, 24 July 2014

Home < Documentation < 4.3 < Extensions < OpenCAD


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


OpenCAD.PNG

Introduction and Acknowledgements

This work is supported by NA-MIC, NCIGT, and the Slicer Community.
Author: Vivek Narayan, Jayender Jagadeesan
Contact: Jayender Jagadeesan <email> jayender@bwh.harvard.edu</email>

NA-MIC  
NCIGT  
SPL  

This project is supported by P41 RR019703/RR/NCRR NIH HHS/United States, P01 CA067165/CA/NCI NIH HHS/United States and P41 EB015898/EB/NIBIB NIH HHS/United States


Modules


Extension Description

  • The SegmentCAD module is designed to segment tumors from DCE-MRI datasets which include a pre-contrast image and post-contrast images at different time points.
    • SegmentCAD uses blackbox methods to calculate the wash-in and wash-out slopes from the time-intensity curves.
    • The segmentation output is a Label Map with red, yellow, and blue colors respectively identifying washout (Type III), plateau (Type II), and persistent (Type I) voxels.
  • The HeterogeneityCAD module is an extensible, image feature extraction toolbox primarily to quantify the heterogeneity of tumor images and their label maps.
    • Metrics have been implemented from a variety of feature classes including:
      • First-Order/Histogram statistics
      • Morphology/Shape measures and Geometrical (4D Extrusion) measures
      • Renyi/Fractal dimensions
      • Texture features computed from Gray-Level Co-occurrence Matrices (GLCM) and from Gray-Level Run Length matrices (GLRL)


Tutorials

Data sets


Quick Instructions for Use

  • SegmentCAD (Click link for detailed description)
    • Select the pre-contrast volume
    • Select the first post-contrast volume
    • Select the second post-contrast volume
    • Select the third post-contrast volume
    • Select the fourth post-contrast volume
    • Create or select a label map volume node to represent the output of the segmentation
    • Click "Apply OpenCAD Segmentation"
  • HeterogeneityCAD (Click link for detailed description)
    • Add an image or parameter map (.nrrd file) to the Nodes List
    • Select a corresponding segmentation label map to use as ROI
    • Click "Apply HeterogeneityCAD"

Similar Modules

  • SegmentCAD:
  • HeterogeneityCAD:
    • LabelStatistics

References

  • J. Jayender, E. Gombos, S. Chikarmane, D. Dabydeen, F. A. Jolesz, and K. G. Vosburgh, “Statistical Learning Algorithm for In-situ and Invasive Breast Carcinoma Segmentation”, Journal of Computerized Medical Imaging and Graphics, vol. 37, no. 4, pp. 281-292, 2013
  • J. Jayender, S. A. Chikarmane, F. A. Jolesz and E. Gombos, “Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis”, Journal of MRI, Article first published online 23 September 2013, doi: 10.1002/jmri.24394
  • J. Jayender, K.G. Vosburgh, E. Gombos, A. Ashraf, D. Kontos, S.C. Gavenonis, F. A. Jolesz and K. Pohl , “Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm”, IEEE International Symposium on Biomedical Imaging, pp. 122-125, 2012.
  • J. Jayender, D.T. Ruan, V. Narayan, N. Agrawal, F. A. Jolesz and H. Mamata, “Segmentation of Parathyroid Tumors from DCE-MRI using Linear Dynamic System Analysis”, IEEE International Symposium on Biomedical Imaging, 2013.
  • J. Jayender, J. Jagannathan, S.Chikarmane, C.P.Raut and F.A. Jolesz, “Computer-Aided Diagnosis of Breast Angiosarcoma: Results in 14 cases”, Quantitative Medical Imaging Symposium, 2013 (invited paper).
  • HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach", vol. 5, Nat Communication, 2014.


Information for Developers

Source code: https://github.com/vnarayan13/Slicer-OpenCAD

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