Ensemble segmentation for GBM brain tumors on MR images using confidence‐based averaging
Jing Huo, Kazunori Okada, Eva M. van Rikxoort, Hyun J. Kim, Jeffry R. Alger, Whitney B. Pope, JONATHAN G GOLDIN, Matthew S. Brown · Medical Physics · 2013
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.