Abstract
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. 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. 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. The results showed that the CMA ensemble result statistically approximates the best segmentation result of all the base methods for each case.
Highlights
Glioblastoma multiforme (GBM), a World Health Organization (WHO) grade IV astrocytoma, is the most common human brain tumor comprising about 12%–15% of all primary central nervous system (CNS) tumors and accounting for about 50%–60% of all astrocytomas.[1]
We calculated the F1-measure for all 46 GBM tumors to evaluate the accuracy of the segmentation results against the ground truth, and to compare the three base methods and our ensemble method
A paired t-test was run to compare the three base methods the results are shown in Table II, indicating that GC and support vector machine (SVM) are significantly better than fuzzy connectedness (FC) method
Summary
Glioblastoma multiforme (GBM), a World Health Organization (WHO) grade IV astrocytoma, is the most common human brain tumor comprising about 12%–15% of all primary central nervous system (CNS) tumors and accounting for about 50%–60% of all astrocytomas.[1]. Contrast-enhanced tumor size change on serial imaging studies is used as a surrogate endpoint using 1D and 2D diameters. Manual contouring has been used to segment tumors on MR images. In a recent clinical study of correlating methylated-DNA-protein-cysteine methyltransferase (MGMT) promoter methylation and imaging features of GBM tumors, Drabycz et al.[3] used manual contouring for GBM brain tumor segmentation.
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