GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Miller, James V.
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CitationEgger, Jan, Tina Kapur, Andriy Fedorov, Steve Pieper, James V. Miller, Harini Veeraraghavan, Bernd Freisleben, Alexandra J. Golby, Christopher Nimsky, and Ron Kikinis. 2013. GBM volumetry using the 3D slicer medical image computing platform. Scientific Reports 3:1364.
AbstractVolumetric 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10611818
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