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 theF1-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.

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