TY - JOUR
T1 - An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
AU - Ali, Sharib
AU - Zhou, Felix
AU - Braden, Barbara
AU - Bailey, Adam
AU - Yang, Suhui
AU - Cheng, Guanju
AU - Zhang, Pengyi
AU - Li, Xiaoqiong
AU - Kayser, Maxime
AU - Soberanis-Mukul, Roger D.
AU - Albarqouni, Shadi
AU - Wang, Xiaokang
AU - Wang, Chunqing
AU - Watanabe, Seiryo
AU - Oksuz, Ilkay
AU - Ning, Qingtian
AU - Yang, Shufan
AU - Khan, Mohammad Azam
AU - Gao, Xiaohong W.
AU - Realdon, Stefano
AU - Loshchenov, Maxim
AU - Schnabel, Julia A.
AU - East, James E.
AU - Wagnieres, Georges
AU - Loschenov, Victor B.
AU - Grisan, Enrico
AU - Daul, Christian
AU - Blondel, Walter
AU - Rittscher, Jens
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079621417&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-59413-5
DO - 10.1038/s41598-020-59413-5
M3 - Article
C2 - 32066744
AN - SCOPUS:85079621417
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 2748
ER -