Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging
SourceMedical Physics, 40, (2013), pp. 093502
Article / Letter to editor
Display more detailsDisplay less details
SubjectONCOL 5: Aetiology, screening and detection
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.
This item appears in the following Collection(s)
- Academic publications 
- Electronic publications 
- Faculty of Medical Sciences 
- Open Access publications 
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.