Confidence-based ensemble for GBM brain tumor segmentation

Jing Huo; Eva M. van Rikxoort; Kazunori Okada; Hyun J. Kim; Whitney Pope; Jonathan Goldin; Matthew Brown

doi:10.1117/12.877913

14 March 2011 Confidence-based ensemble for GBM brain tumor segmentation

Jing Huo, Eva M. van Rikxoort, Kazunori Okada, Hyun J. Kim, Whitney Pope, Jonathan Goldin, Matthew Brown

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79622P (2011) https://doi.org/10.1117/12.877913
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Jing Huo, Eva M. van Rikxoort, Kazunori Okada, Hyun J. Kim, Whitney Pope, Jonathan Goldin, and Matthew Brown "Confidence-based ensemble for GBM brain tumor segmentation", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622P (14 March 2011); https://doi.org/10.1117/12.877913

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