Difference between revisions of "Documentation/Nightly/Extensions/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/{{documentation/version}}/extension-section|Modules}}
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The {{documentation/modulename}} is designed to segment tumors from DCE-MRI datasets which include 1 pre-contrast image and 2-4 post-contrast images at different time points. {{documentation/modulename}} uses blackbox methods to calculate the wash-in and wash-out slopes of the contrast dye based on voxel intensity values. The segmentation output is a Label Map with red, yellow, and blue colors respectively identifying washout, plateau, and persistent voxels.
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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: Feature Extraction toolbox for image heterogeneity analysis]]
  
  
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Red for washout curve
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{{documentation/{{documentation/version}}/extension-section|Extension Description}}
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(Sd < -0.2 | Type III)
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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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Yellow for plateau curve
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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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(-0.2 < Sd < 0.2 | Type II)
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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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Blue for persistent curve
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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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(Sd > 0.2 | Type I)  
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**Metrics have been implemented from a variety of feature classes including:
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-->
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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|Features}}
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*Interactive Charting -  
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*Volume Rendering -
 
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*Label Statistics -  
 
  
 
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{{documentation/{{documentation/version}}/module-section|Tutorials}}
 
{{documentation/{{documentation/version}}/module-section|Tutorials}}
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[[Media:OpenCADTutorial.pptx|{{documentation/modulename}} Tutorial (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-data1.zip|Breast DCE-MRI Data Set 1 (zip file containing the nrrd volumes for the {{documentation/modulename}} tutorial)]]‎  
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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-data2.zip|Breast DCE-MRI Data Set 2 (zip file containing additional test set of 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:Liver-data1.zip|Liver DCE-MRI Data Set 1 (zip file containing additional test set of nrrd volumes)]]‎
 
  
 
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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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|
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**Select the pre-contrast volume
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The GUI of the {{documentation/modulename}} module contains 5 sections:
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**Select the first post-contrast volume
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* '''Select DCE-MRI Volumes for Segmentation'''
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**Select the second post-contrast volume
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** '''Pre-contrast Volume:'''
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**Select the third post-contrast volume
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** '''First Post-contrast Volume:'''
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**Select the fourth post-contrast volume
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** '''Second 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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** '''Third Post-contrast Volume:'''
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**Click "Apply OpenCAD Segmentation"
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** '''Fourth Post-contrast Volume:'''
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*[[Documentation/{{documentation/version}}/Modules/HeterogeneityCAD|HeterogeneityCAD (Click link for detailed description)]]
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** '''Use Label Map as ROI:'''
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**Add an image or parameter map (.nrrd file) to the Nodes List
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* '''Select or Create Output OpenCAD Label Map'''
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**Select a corresponding segmentation label map to use as ROI
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**'''Output OpenCAD Label Map:'''
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**Click "Apply HeterogeneityCAD"
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**'''Legend | |:'''
 
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**'''Display Volume Rendering:'''
 
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**'''Calculate OpenCAD Label statistics:'''
 
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* '''Set Advanced Segmentation Parameters'''
 
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**'''Minimum Threshold of Increase:'''
 
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**'''Type I (Persistent) Curve Minimum Slope:'''
 
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**'''Type 3 (Washout) Curve Maximum Slope:'''
 
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* '''OpenCAD Label Statistics'''
 
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**'''Statistics Table:'''
 
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**'''Chart Statistics:'''
 
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***'''Menu Items:''' Volume, Curve Type, Voxel Count, Volume mm^3, Volume cc, Minimum Intensity, Maximum Intensity, Mean Intensity, Standard Deviation
 
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***'''Ignore Zero label:'''
 
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* '''Interactive Charting Settings'''
 
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**'''Enable/Disable Interactive Charting:'''
 
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|[[Image:OpenCAD-GUI.png|thumb|280px|{{documentation/modulename}} GUI ]]
 
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|}
 
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{{documentation/{{documentation/version}}/module-parametersdescription}}
 
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-->
 
  
 
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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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Source code: https://github.com/vnarayan13/Slicer-OpenCAD
 
Source code: https://github.com/vnarayan13/Slicer-OpenCAD
 
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{{documentation/{{documentation/version}}/module-footer}}
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Latest revision as of 03:46, 2 August 2014

Home < Documentation < Nightly < 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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