An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.
Fiche publication
Date publication
février 2020
Journal
Scientific reports
Auteurs
Membres identifiés du Cancéropôle Est :
Dr BLONDEL Walter, Pr DAUL Christian
Tous les auteurs :
Ali S, Zhou F, Braden B, Bailey A, Yang S, Cheng G, Zhang P, Li X, Kayser M, Soberanis-Mukul RD, Albarqouni S, Wang X, Wang C, Watanabe S, Oksuz I, Ning Q, Yang S, Khan MA, Gao XW, Realdon S, Loshchenov M, Schnabel JA, East JE, Wagnieres G, Loschenov VB, Grisan E, Daul C, Blondel W, Rittscher J
Lien Pubmed
Résumé
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.
Référence
Sci Rep. 2020 Feb 17;10(1):2748