How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-experts, and Algorithms

Analyses of biomedical images often rely on demarcating the boundaries of biological structures (segmentation). While numerous approaches are adopted to address the segmentation problem including collecting annotations from domain-experts and automated algorithms, the lack of comparative benchmarking makes it challenging to determine the current state-of-art, recognize limitations of existing approaches, and identify relevant future research directions. To provide practical guidance, we evaluated and compared the performance of trained experts, crowd sourced non-experts, and algorithms for annotating 305 objects coming from six datasets that include phase contrast, fluorescence, and magnetic resonance images. Compared to the gold standard established by expert consensus, we found the best annotators were experts, followed by non-experts, and then algorithms. This analysis revealed that online paid crowd sourced workers without domain-specific backgrounds are reliable annotators to use as part of the laboratory protocol for segmenting biomedical images. We also found that fusing the segmentations created by crowd sourced internet workers and algorithms yielded improved segmentation results over segmentations created by single crowd sourced or algorithm annotations respectively. We invite extensions of our work by sharing our data sets and associated segmentation annotations (http://www.cs.bu.edu/~betke/Biomedical Image Segmentation).