Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging

J. Huo, K. Okada, E.M. van Rikxoort, H.J. Kim, J.R. Alger, W.B. Pope, J.G. Goldin and M.S. Brown

Medical Physics 2013;40(9):093502



Purpose: Ensemble segmentation methods combine the segmentation results of individual methods into a final one, with the goal of achieving greater robustness and accuracy. The goal of this study was to develop an ensemble segmentation framework for glioblastoma multiforme tumors on single-channel T1w postcontrast magnetic resonance images.Methods: Three base methods were evaluated in the framework: fuzzy connectedness, GrowCut, and voxel classification using support vector machine. A confidence map averaging (CMA) method was used as the ensemble rule.Results: The performance is evaluated on a comprehensive dataset of 46 cases including different tumor appearances. The accuracy of the segmentation result was evaluated using the F1-measure between the semiautomated segmentation result and the ground truth.Conclusions: The results showed that the CMA ensemble result statistically approximates the best segmentation result of all the base methods for each case.