An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

Sharib Ali, Felix Zhou, Barbara Braden, Adam Bailey, Suhui Yang, Guanju Cheng, Pengyi Zhang, Xiaoqiong Li, Maxime Kayser, Roger D Soberanis-Mukul, Shadi Albarqouni, Xiaokang Wang, Chunqing Wang, Seiryo Watanabe, Ilkay Oksuz, Qingtian Ning, Shufan Yang, Mohammad Azam Khan, Xiaohong W Gao, Stefano RealdonMaxim Loshchenov, Julia A Schnabel, James E East, Georges Wagnieres, Victor B Loschenov, Enrico Grisan, Christian Daul, Walter Blondel, Jens Rittscher

Research output: Contribution to journalArticlepeer-review


We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art computer vision methods applied to endoscopy and promoting the development of new approaches suitable for clinical translation. Endoscopy is a routine imaging technique for the detection, diagnosis and treatment of diseases in hollow-organs; the esophagus, stomach, colon, uterus and the bladder. However the nature of these organs prevent imaged tissues to be free of imaging artefacts such as bubbles, pixel saturation, organ specularity and debris, all of which pose substantial challenges for any quantitative analysis. Consequently, the potential for improved clinical outcomes through quantitative assessment of abnormal mucosal surface observed in endoscopy videos is presently not realized accurately. The EAD challenge promotes awareness of and addresses this key bottleneck problem by investigating methods that can accurately classify, localize and segment artefacts in endoscopy frames as critical prerequisite tasks. Using a diverse curated multi-institutional, multi-modality, multi-organ dataset of video frames, the accuracy and performance of 23 algorithms were objectively ranked for artefact detection and segmentation. The ability of methods to generalize to unseen datasets was also evaluated. The best performing methods (top 15%) propose deep learning strategies to reconcile variabilities in artefact appearance with respect to size, modality, occurrence and organ type. However, no single method outperformed across all tasks. Detailed analyses reveal the shortcomings of current training strategies and highlight the need for developing new optimal metrics to accurately quantify the clinical applicability of methods.

Original languageEnglish
Article number2748
Pages (from-to)1-15
Number of pages15
JournalSci. Rep.
Issue number1
Publication statusPublished - Feb 17 2020


  • Algorithms
  • Artifacts
  • Colon/diagnostic imaging
  • Datasets as Topic
  • Endoscopy/standards
  • Esophagus/diagnostic imaging
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted/standards
  • Imaging, Three-Dimensional/standards
  • International Cooperation
  • Male
  • Neural Networks, Computer
  • Stomach/diagnostic imaging
  • Urinary Bladder/diagnostic imaging
  • Uterus/diagnostic imaging


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