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Proceedings Paper

Confidence-based ensemble for GBM brain tumor segmentation
Author(s): Jing Huo; Eva M. van Rikxoort; Kazunori Okada; Hyun J. Kim; Whitney Pope; Jonathan Goldin; Matthew Brown
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Paper Abstract

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.

Paper Details

Date Published: 14 March 2011
PDF: 6 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622P (14 March 2011); doi: 10.1117/12.877913
Show Author Affiliations
Jing Huo, Univ. of California, Los Angeles (United States)
Eva M. van Rikxoort, Univ. of California, Los Angeles (United States)
Kazunori Okada, San Francisco State Univ. (United States)
Hyun J. Kim, Univ. of California, Los Angeles (United States)
Whitney Pope, Univ. of California, Los Angeles (United States)
Jonathan Goldin, Univ. of California, Los Angeles (United States)
Matthew Brown, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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