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A crowdsourcing semi-automatic image segmentation platform for cell biology.

Saber Mirzaee Bafti1, Chee Siang Ang1, Md Moinul Hossain1

  • 1School of Engineering and Digital Arts, University of Kent, Canterbury, CT2 7NZ, UK.

Computers in Biology and Medicine
|January 11, 2021
PubMed
Summary

Annotating microbiological images is challenging. A new platform with assistive tools helps non-experts efficiently and accurately label images, reducing costs while maintaining high quality.

Keywords:
Computational biologyCrowdsourcingImage annotationInstance segmentationObject detectionSemi-auto segmentation

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Area of Science:

  • Computer Vision
  • Bioinformatics
  • Medical Imaging

Background:

  • Accurate annotation of large datasets is crucial for computer vision but is resource-intensive.
  • Microbiological image annotation requires specialized expertise, increasing costs and time.
  • Crowdsourcing and assistive tools offer potential solutions for efficient data annotation.

Purpose of the Study:

  • To develop and evaluate a web-based platform for crowdsourced annotation of microbiological images.
  • To assess the impact of a semi-automated assistive tool on non-expert annotator efficiency and quality.
  • To compare non-expert annotations with expert annotations for microbiological image data.

Main Methods:

  • Development of a web-based platform integrating crowdsourcing and a semi-automated assistive tool.
  • Annotation of microbiological gut parasite images by non-expert users with and without the assistive tool.
  • Quantitative evaluation of annotation quality using Intersection over Union (IoU), precision, and recall.

Main Results:

  • The assistive tool significantly decreased annotation costs (time, clicks, interaction) for non-expert annotators.
  • Annotation quality was preserved or improved by using the assistive tool.
  • Analysis provided insights into the effectiveness of crowdsourcing and assistive platforms for image annotation.

Conclusions:

  • Assistive tools are effective in reducing the cost of non-expert annotation for complex image data.
  • The developed platform demonstrates the potential of combining crowdsourcing with assistive technology for scientific image annotation.
  • Findings offer guidance for designing future crowdsourcing and assistive annotation systems.