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

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Abstract

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 F 1-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. © 2013 © 2013 Author(s).

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Huo, J., Okada, K., Van Rikxoort, E. M., Kim, H. J., Alger, J. R., Pope, W. B., … Brown, M. S. (2013). Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging. Medical Physics, 40(9). https://doi.org/10.1118/1.4817475

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