Fast, versatile and quantitative annotation of complex images
| Autor: | Daniel I. Goldman, Melinda L. Jue, Emily Jackson-Holmes, Kathleen Bates, Shivesh Chaudhary, Hang Lu, Shen Jiang, Erin McCaskey |
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| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Time Factors business.industry Computer science Movement 010401 analytical chemistry Mobile Applications 01 natural sciences General Biochemistry Genetics and Molecular Biology 0104 chemical sciences 03 medical and health sciences Annotation Software Human–computer interaction Simple (abstract algebra) Smartphone app Image Processing Computer-Assisted Animals Humans Smartphone Caenorhabditis elegans business 030304 developmental biology Biotechnology |
| Zdroj: | BioTechniques. 66:269-275 |
| ISSN: | 1940-9818 0736-6205 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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