Improving semi-automated segmentation by integrating learning with active sampling

Jing Huo; Kazunori Okada; Matthew Brown

doi:10.1117/12.910973

14 February 2012 Improving semi-automated segmentation by integrating learning with active sampling

Jing Huo, Kazunori Okada, Matthew Brown

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83142M (2012) https://doi.org/10.1117/12.910973
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

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.

Citation Download Citation

Jing Huo, Kazunori Okada, and Matthew Brown "Improving semi-automated segmentation by integrating learning with active sampling", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142M (14 February 2012); https://doi.org/10.1117/12.910973

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