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Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Debesh Jha1, Vanshali Sharma2, Debapriya Banik3

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Deep learning aids colonoscopy by improving polyp detection accuracy. Competitions like Medico 2020 and MedAI 2021 advanced AI segmentation and transparency for better patient care.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Colonoscopy is vital for early precancerous polyp detection, but human error leads to high miss-rates.
  • Automated systems are needed to assist endoscopists, improving diagnostic accuracy and patient outcomes.
  • Transparency and reproducibility are critical for clinical adoption of AI in medicine.

Purpose of the Study:

  • To assess advancements in AI for colonoscopy image analysis through organized competitions.
  • To evaluate the performance and transparency of deep learning models for polyp and instrument segmentation.
  • To encourage the development of reliable and interpretable AI tools for clinical use.

Main Methods:

  • Organized "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions.
  • Collected and analyzed submissions from 17 teams in each competition.
  • Evaluated segmentation performance using Dice coefficient and Intersection over Union metrics.
  • Assessed model transparency through multi-disciplinary expert review of open-source practices, failure analysis, and usability.

Main Results:

  • Improved Dice coefficient for polyp segmentation from 0.8607 (2020) to 0.8993 (2021) on challenging cases.
  • Achieved a mean Intersection over Union of 0.9364 for surgical instrument segmentation.
  • The top team for transparency achieved a score of 21/25, demonstrating progress in model interpretability.

Conclusions:

  • AI-driven polyp and instrument segmentation in colonoscopy has significantly advanced.
  • Increased transparency and rigorous evaluation are essential for trustworthy clinical AI deployment.
  • Further multi-center and out-of-distribution testing is needed to ensure robustness and reduce cancer burden.