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Crowd sourcing image segmentation with iaSTAPLE

Item Type:Conference or Workshop Item
Title:Crowd sourcing image segmentation with iaSTAPLE
Creators Name:Schlesinger, D., Jug, F., Myers, G., Rother, C. and Kainmueller, D.
Abstract:We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.
Keywords:Epithelial Cell Segmentation, Crowdsourcing, Markovian Random Fields, IaSTAPLE
Title of Book:2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Page Range:401-405
Date:19 June 2017
Official Publication:https://doi.org/10.1109/ISBI.2017.7950547

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