https://www.slicer.org/w/index.php?action=history&feed=atom&title=Documentation%2F4.8%2FExtensions%2FOpenCADDocumentation/4.8/Extensions/OpenCAD - Revision history2026-04-13T08:38:46ZRevision history for this page on the wikiMediaWiki 1.33.0https://www.slicer.org/w/index.php?title=Documentation/4.8/Extensions/OpenCAD&diff=55173&oldid=prevUpdateBot: Nightly -> 4.82017-10-18T07:01:07Z<p>Nightly -> 4.8</p>
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
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This work is supported by NA-MIC, NCIGT, and the Slicer Community.<br><br />
Author: Vivek Narayan, Jayender Jagadeesan<br><br />
Contact: Jayender Jagadeesan <email> jayender@bwh.harvard.edu</email> <br><br />
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|{{collaborator|logo|namic}}|NA-MIC<br />
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|{{collaborator|logo|spl}}|SPL<br />
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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<br />
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*[[Documentation/{{documentation/version}}/Modules/SegmentCAD|SegmentCAD: Tumor Segmentation from DCE-MRI]]<br />
*[[Documentation/{{documentation/version}}/Modules/HeterogeneityCAD|HeterogeneityCAD: Feature Extraction toolbox for image heterogeneity analysis]]<br />
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{{documentation/{{documentation/version}}/extension-section|Extension Description}}<br />
*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. <br />
**SegmentCAD uses blackbox methods to calculate the wash-in and wash-out slopes from the time-intensity curves. <br />
**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.<br />
*The HeterogeneityCAD module is an extensible, image feature extraction toolbox primarily to quantify the heterogeneity of tumor images and their label maps. <br />
**Metrics have been implemented from a variety of feature classes including: <br />
***First-Order/Histogram statistics<br />
***Morphology/Shape measures and Geometrical (4D Extrusion) measures<br />
***Renyi/Fractal dimensions<br />
***Texture features computed from Gray-Level Co-occurrence Matrices (GLCM) and from Gray-Level Run Length matrices (GLRL)<br />
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*SegmentCAD<br />
**[[Media:SegmentCADTutorial.pptx|SegmentCAD Tutorial (pptx)]]<br />
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*SegmentCAD<br />
**[[Media:Breast-data1.zip|Breast DCE-MRI Data Set 1 (zip file containing the nrrd volumes for the tutorial)]] <br />
**[[Media:Breast-data2.zip|Breast DCE-MRI Data Set 2 (zip file containing additional test set of nrrd volumes)]]<br />
*HeterogeneityCAD<br />
**[[Media:BreastHeteroCADData.zip|Breast DCE-MRI Data Set (zip file containing the nrrd volumes for the tutorial)]] <br />
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*[[Documentation/{{documentation/version}}/Modules/SegmentCAD|SegmentCAD (Click link for detailed description)]]<br />
**Select the pre-contrast volume<br />
**Select the first post-contrast volume<br />
**Select the second post-contrast volume<br />
**Select the third post-contrast volume<br />
**Select the fourth post-contrast volume<br />
**Create or select a label map volume node to represent the output of the segmentation<br />
**Click "Apply OpenCAD Segmentation"<br />
*[[Documentation/{{documentation/version}}/Modules/HeterogeneityCAD|HeterogeneityCAD (Click link for detailed description)]]<br />
**Add an image or parameter map (.nrrd file) to the Nodes List<br />
**Select a corresponding segmentation label map to use as ROI<br />
**Click "Apply HeterogeneityCAD"<br />
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*SegmentCAD:<br />
*HeterogeneityCAD:<br />
**LabelStatistics<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
* 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 <br />
* 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 <br />
* 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.<br />
* 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.<br />
* 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).<br />
* HJWL Aerts, ER Velazquez, RTH Leijenaar, et al., "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach", vol. 5, Nat Communication, 2014.<br />
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Source code: https://github.com/vnarayan13/Slicer-OpenCAD<br />
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