An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
Authors: Xiaokang Wang
Xiaohong W Gao
Victor B Loschenov
Christian Daul
Julia A Schnabel
Qingtian Ning
Shufan Yang
Chunqing Wang
Pengyi Zhang
Suhui Yang
Sharib Ali
Georges Wagnieres
Mohammad Azam Khan
Barbara Braden
Shadi Albarqouni
Jens Rittscher
Seiryo Watanabe
Walter Blondel
Enrico Grisan
Roger D Soberanis-Mukul
Ilkay Oksuz
James E East
Adam Bailey
Felix Zhou
Stefano Realdon
Maxime Kayser
Guanju Cheng
Maxim Loshchenov
Xiaoqiong Li
Journal: Scientific Reports (2020)
Discipline: Multidisciplinary
Keywords: Endoscopy; Artefact Detection; Computer Vision; Clinical Translation; Imaging Technique
Affiliations: Institute of Biomedical Engineering, Big Data Institute, Department of Engineering Science, University of Oxford, Oxford, UK. ., Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK., Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Div., John Radcliffe Hospital, University of Oxford, Oxford, UK., Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China., Beijing Institute of Technology, Beijing, China., Technishe Universität München, Munich, Germany., Department of Biomedical Engineering, University of California, Davis, USA., Department of Ultrasound Imaging, Tiantan Hospital, Beijing, China., Department of Bioinformatic Engineering, Osaka University, Suita, Osaka, Japan., School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK., Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey., Department of Automation, Shanghai Jiao Tong University, Shanghai, China., School of Engineering, University of Glasgow, Glasgow, UK., Department of Computer Science and Engineering, Korea University, Seoul, South Korea., Department of Computer Science, Middlesex University, London, UK., Instituto Onclologico Veneto, IOV-IRCCS, Padova, Italy., A.M. Prokhorov General Physics Institute, Russian Academy of Science, Moscow, Russia., Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland., Department of Information Engineering, University of Padova, Padova, Italy., School of Engineering, London South Bank University, London, UK., CRAN UMR 7039, University of Lorraine, CNRS, Nancy, France., Institute of Biomedical Engineering, Big Data Institute, Department of Engineering Science, University of Oxford, Oxford, UK.

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. 

Retracted: No

PubPeer comments:
 This publication refers to the following work from a subgroup of its authors as an example of 2D mosaicing (reference [4]): [Fast mosaicing of cystoscopic images from dense correspondence: Combined SURF and TV-L1 optical flow method by Sharib Ali et al. in ICIP (2013)] . This is just an initial work in a series of publications on this topic from these authors. The later publications propose some quite innovative approaches and report some remarkable results, as is pointed out on the PubPeer pages of these publications: [Robust and accurate optical flow estimation for weak texture and varying illumination conditions: Application to cystoscopy by Sharib Ali et al. in IPTA (2014)] [Anisotropic motion estimation on edge preserving Riesz wavelets for robust video mosaicing by Sharib Ali et al. in Pattern Recognition (2016)] [Optical flow with structure information for epithelial image mosaicing by Sharib Ali et al. in EMBC (2015)] [Robust bladder image registration by redefining data-term in total variational approach by Sharib Ali et al. in SPIE (2015)] [Illumination invariant optical flow using neighborhood descriptors by Sharib Ali et al. in CVIU (2016)] Is there any particular reason for which the 2013 ICIP paper was chosen over the other more detailed work from the same authors? ,