Quanti.us: a tool for rapid, flexible, crowd-based annotation of images

Nat Methods. 2018 Aug;15(8):587-590. doi: 10.1038/s41592-018-0069-0. Epub 2018 Jul 31.


We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Animals
  • Computational Biology / methods
  • Crowdsourcing / methods
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods
  • Internet
  • Machine Learning
  • Software*