  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.
