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Proceedings Paper

Sampling-based ensemble segmentation against inter-operator variability
Author(s): Jing Huo; Kazunori Okada; Whitney Pope; Matthew Brown
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Paper Abstract

Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM) brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu thresholding method (p<.001).

Paper Details

Date Published: 5 March 2011
PDF: 7 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796315 (5 March 2011); doi: 10.1117/12.878338
Show Author Affiliations
Jing Huo, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Kazunori Okada, San Francisco State Univ. (United States)
Whitney Pope, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Matthew Brown, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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