Sampling-based ensemble segmentation against inter-operator variability

Jing Huo; Kazunori Okada; Whitney Pope; Matthew Brown

doi:10.1117/12.878338

4 March 2011 Sampling-based ensemble segmentation against inter-operator variability

Jing Huo, Kazunori Okada, Whitney Pope, Matthew Brown

Author Affiliations +

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796315 (2011) https://doi.org/10.1117/12.878338
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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).

Citation Download Citation

Jing Huo, Kazunori Okada, Whitney Pope, and Matthew Brown "Sampling-based ensemble segmentation against inter-operator variability", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796315 (4 March 2011); https://doi.org/10.1117/12.878338

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