Difference between revisions of "2012 Winter Project Week:GBMseg"

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==Key Investigators==
 
==Key Investigators==
 
* WUSM: Dan Marcus, Misha Milchenko
 
* WUSM: Dan Marcus, Misha Milchenko
* Brigham and Women's: Andrey Fedorov
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* Brigham and Women's: Andrey Fedorov, Jan Egger
  
 
<div style="margin: 20px;">
 
<div style="margin: 20px;">
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<h3>Objective</h3>
 
<h3>Objective</h3>
We are looking to segment and visualize GBM's from various MR sequences in Slicer, and create an integrated display for clinisian review, both radiologist and non-radiologist.
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We are looking to segment and visualize GBM's from various MR sequences in Slicer, and create an integrated display for clinician review, both radiologist and non-radiologist.
  
 
</div>
 
</div>
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
The approach is to obtain accurate registration of a number of MR sequences of each tumor case, and analyze correlation with pathology data.
+
The approach is to obtain accurate registration of a number of MR sequences of each tumor case, segment what is thought to be the tumor on T1 post contrast and analyze correlation with pathology data.
  
 
</div>
 
</div>
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<h3>Progress</h3>
 
<h3>Progress</h3>
 +
 +
Segmented one T2 FLAIR image (W009), voxel size 1x1x5mm using growcut region algorithm. Manual pre-labeling was done on approximately central slice of the tumor in three section planes separately. Two-class tissue model (enhancing and non-enhancing region) was used.
 +
Results: visually, the algorithm picked out most (about 80%) of the enhancing region. Some sharp mass processes were missed, as well as some areas that subjectively might not belong to tumor were also attributed to tumor. Comparing with hi-res T1 post-contrast (1x1x2mm voxel), revealed significant areas that highlight differently on T1 and T2, esp. around tumor boundary. Another accuracy limiting factor was high anisotropy of T2 acquisition, which resulted in ghosted data, particularly observabe in the areas of low signal, such as adjacent to bone.
 +
 
</div>
 
</div>
 
</div>
 
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==Delivery Mechanism==
 
==Delivery Mechanism==
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GBM MR preprocessing pipeline
  
 
==References==
 
==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
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GrowCut segmentation Slicer documentation: http://wiki.slicer.org/slicerWiki/index.php/Modules:GrowCutSegmentation-Documentation-3.6
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
+
 
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
+
V. Vezhnevets and V. Konouchine, "GrowCut - Interactive multi-label N-D image segmentation," in Proc. Graphicon, 2005. pp. 150--156.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .
 
  
 +
Mark Jenkinson, Stephen Smith. "Optimisation in Robust Linear Registration of Brain Images." FMRIB Technical Report TR00MJ2, http://www.fmrib.ox.ac.uk/analysis/techrep/tr00mj2/tr00mj2/
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</div>
 
</div>

Latest revision as of 17:21, 10 January 2012

Home < 2012 Winter Project Week:GBMseg

Key Investigators

  • WUSM: Dan Marcus, Misha Milchenko
  • Brigham and Women's: Andrey Fedorov, Jan Egger

Objective

We are looking to segment and visualize GBM's from various MR sequences in Slicer, and create an integrated display for clinician review, both radiologist and non-radiologist.

Approach, Plan

The approach is to obtain accurate registration of a number of MR sequences of each tumor case, segment what is thought to be the tumor on T1 post contrast and analyze correlation with pathology data.

Progress

Segmented one T2 FLAIR image (W009), voxel size 1x1x5mm using growcut region algorithm. Manual pre-labeling was done on approximately central slice of the tumor in three section planes separately. Two-class tissue model (enhancing and non-enhancing region) was used. Results: visually, the algorithm picked out most (about 80%) of the enhancing region. Some sharp mass processes were missed, as well as some areas that subjectively might not belong to tumor were also attributed to tumor. Comparing with hi-res T1 post-contrast (1x1x2mm voxel), revealed significant areas that highlight differently on T1 and T2, esp. around tumor boundary. Another accuracy limiting factor was high anisotropy of T2 acquisition, which resulted in ghosted data, particularly observabe in the areas of low signal, such as adjacent to bone.

Delivery Mechanism

GBM MR preprocessing pipeline

References

GrowCut segmentation Slicer documentation: http://wiki.slicer.org/slicerWiki/index.php/Modules:GrowCutSegmentation-Documentation-3.6

V. Vezhnevets and V. Konouchine, "GrowCut - Interactive multi-label N-D image segmentation," in Proc. Graphicon, 2005. pp. 150--156.

Mark Jenkinson, Stephen Smith. "Optimisation in Robust Linear Registration of Brain Images." FMRIB Technical Report TR00MJ2, http://www.fmrib.ox.ac.uk/analysis/techrep/tr00mj2/tr00mj2/