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Proceedings Paper

Improving semi-automated segmentation by integrating learning with active sampling
Author(s): Jing Huo; Kazunori Okada; Matthew Brown
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Paper 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.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142M (14 February 2012); doi: 10.1117/12.910973
Show Author Affiliations
Jing Huo, Univ. of California, Los Angeles (United States)
Kazunori Okada, San Francisco State Univ. (United States)
Matthew Brown, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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