Mixing Crowd and Algorithm Efforts to Segment Objects in Biomedical Images

نویسندگان

  • Danna Gurari
  • Mehrnoosh Sameki
  • Zheng Wu
  • Margrit Betke
چکیده

We examine how to leverage the efforts of crowds and algorithms to find the boundaries of objects in biomedical images (segmentation). We propose a modular framework, SAVE, which generates candidate segmentations and then employs voting to choose among the candidates. This framework supports integration of both computer vision and crowdsourcing modules. We evaluated four implementations of SAVE with different combinations of efforts from crowd workers and algorithms and compared the resulting quality to segmentations created by experts, crowds, and algorithms on 305 biomedical images. Our experiments demonstrate how to produce segmentations more accurate than relying on algorithms or crowd workers alone and comparable (statistically similar) in quality to segmentations created by biomedical experts.

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تاریخ انتشار 2016