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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Fast, versatile and quantitative annotation of complex images.

Kathleen Bates1,2, Shen Jiang3, Shivesh Chaudhary2

  • 1Interdisciplinary Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA, USA.

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|April 25, 2019
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A new smartphone app enables rapid, crowdsourced annotation of complex scientific images. This tool significantly speeds up image analysis for research, including plant biology and developmental studies.

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behaviorimage analysisroot systemssmartphone appsoftwarestem cell aggregates

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Area of Science:

  • Biotechnology
  • Computer Science
  • Scientific Imaging

Background:

  • Quantitative annotation of complex images is crucial for scientific research.
  • Current methods can be time-consuming and labor-intensive.
  • There is a need for efficient and accessible image analysis tools.

Purpose of the Study:

  • To develop and validate a generic smartphone application for quantitative image annotation.
  • To assess the efficiency and accuracy of the app for diverse biological datasets.
  • To leverage crowdsourcing for accelerated image analysis.

Main Methods:

  • A user-friendly smartphone application was developed for image annotation.
  • Annotation tasks were distributed among multiple app users (crowdsourcing).
  • The app was tested on over 30,000 images across various biological domains.

Main Results:

  • The app demonstrated high flexibility and speed in annotating complex images.
  • Annotation speed was over 130-fold faster than state-of-the-art techniques.
  • The accuracy of annotations was comparable to existing methods.

Conclusions:

  • The developed smartphone app offers a highly efficient and scalable solution for quantitative image annotation.
  • Crowdsourced annotation via a simple app can significantly accelerate scientific discovery.
  • This approach has broad applicability in fields requiring analysis of complex biological images.