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GBM Volumetry using the 3D Slicer Medical Image Computing Platform

1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Neurosurgery, University Hospital of Marburg, Marburg, Germany.
3Department of Mathematics and Computer Science, The Philipps-University of Marburg, Marburg, Germany.
4Interventional and Therapy Lab, GE Research, Niskayuna, NY, USA.
5Biomedical Image Analysis Lab, GE Research, Niskayuna, NY, USA.
6Surgical Planning Laboratory, Brigham and Women´s Hospital and Harvard Medical School, Boston, MA, USA.
Nature Publishing Group
Publication Date:
Sci Rep
Volume Number:
Sci Rep. 2013 Mar 4;3:1364.
PubMed ID:
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
R03 EB013792/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Egger J., Kapur T., Fedorov A., Pieper S., Miller J.V., Veeraraghavan H., Freisleben B., Golby A.J., Nimsky C., Kikinis R. GBM Volumetry using the 3D Slicer Medical Image Computing Platform. Sci Rep. 2013 Mar 4;3:1364. PMID: 23455483. PMCID: PMC3586703.
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Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm.

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