Fast, Versatile and Quantitative Annotation of Complex Images | Zendy

Kathleen Bates | Zendy; Shen Jiang | Zendy; Shivesh Chaudhary | Zendy; Emily JacksonHolmes | Zendy; Melinda L. Jue | Zendy; Erin McCaskey | Zendy; Daniel I. Goldman | Zendy; Hang Lu | Zendy

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Fast, Versatile and Quantitative Annotation of Complex Images

Author(s) -

Kathleen Bates,

Shen Jiang,

Shivesh Chaudhary,

Emily JacksonHolmes,

Melinda L. Jue,

Erin McCaskey,

Daniel I. Goldman,

Hang Lu

Publication year - 2019

Publication title -

biotechniques

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.617

H-Index - 131

eISSN - 1940-9818

pISSN - 0736-6205

DOI - 10.2144/btn-2019-0010

Subject(s) - annotation , computer science , caenorhabditis elegans , flexibility (engineering) , automatic image annotation , image retrieval , aggregate (composite) , artificial intelligence , computational biology , computer vision , biology , image (mathematics) , genetics , mathematics , statistics , gene , materials science , composite material

We report a generic smartphone app for quantitative annotation of complex images. The app is simple enough to be used by children, and annotation tasks are distributed across app users, contributing to efficient annotation. We demonstrate its flexibility and speed by annotating >30,000 images, including features of rice root growth and structure, stem cell aggregate morphology, and complex worm (Caenorhabditis elegans) postures, for which we show that the speed of annotation is >130-fold faster than state-of-the-art techniques with similar accuracy.

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