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

Ali, S. et al. (2020) An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific Reports, 10, 2748. (doi: 10.1038/s41598-020-59413-5) (PMID:32066744) (PMCID:PMC7026422)

Abstract

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.

• Item Type: Articles
• Additional Information: The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). Parts of this work was also supported by MedIAN network (EPSRC EP/N026993/1) and Cancer Research UK. SA, BB, AB and JEE is supported by NIHR BRC, FYZ by Ludwig Institute for Cancer Research (LICR) and JR by LICR and EPSRC Seebibyte Programme Grant (EP/M013774/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yang, Dr Shufan
• Authors: Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R. D., Albarqouni, S., Wang, X., Wang, C., Watanabe, S., Oksuz, I., Ning, Q., Yang, S., Khan, M. A., Gao, X. W., Realdon, S., Loshchenov, M., Schnabel, J. A., East, J. E., Wagnieres, G., Loschenov, V. B., Grisan, E., Daul, C., Blondel, W., and Rittscher, J.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Published Online: 17 February 2020
• Copyright Holders: Copyright © The Authors 2020
• First Published: First published in Scientific Reports 10:2748
• Publisher Policy: Reproduced under a Creative Commons license