GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Citation:

Jan Egger, 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.” Sci Rep, 3, Pp. 1364.

Abstract:

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.
Last updated on 10/19/2017