14 February 2012 Improving semi-automated segmentation by integrating learning with active sampling
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Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to perform well, and have poor repeatability. In this study, we developed a novel technique to boost the performance of the interactive segmentation method GrowCut involving: 1) a novel "focused sampling" approach for supervised learning, as opposed to conventional random sampling; 2) boosting GrowCut using the machine learned results. We applied the proposed technique to the glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases from a multiple center pharmaceutical drug trial. The results showed that the proposed system has the potential to reduce user interaction while maintaining similar segmentation accuracy.
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Jing Huo, Jing Huo, Kazunori Okada, Kazunori Okada, Matthew Brown, Matthew Brown, "Improving semi-automated segmentation by integrating learning with active sampling", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142M (14 February 2012); doi: 10.1117/12.910973; https://doi.org/10.1117/12.910973

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