Confidence-based ensemble for GBM brain tumor segmentation

نویسندگان

  • Jing Huo
  • Eva M. van Rikxoort
  • Kazunori Okada
  • Hyun J. Kim
  • Whitney B. Pope
  • Jonathan G. Goldin
  • Matthew S. Brown
چکیده

It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

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تاریخ انتشار 2011