Endoscopy Artefact Detection (EAD) Dataset (includes updated 2020 version)

Published: 1 March 2021| Version 4 | DOI: 10.17632/c7fjbxcgj9.4
Sharib Ali,
Mariia Dmitrieva,
Felix Zhou,
Christian Daul,
Barbara Braden,
Adam Bailey,
James East,
Stefano Realdon,
Wagnieres Georges,
Maxim Loshchenov,
Walter Blondel,
Enrico Grisan,
Jens Rittscher


We aim to establish a first large and comprehensive dataset for "Endoscopy artefact detection". The released data is a part of Endoscopy Artefact Detection IEEE ISBI challenge. With the Endoscopy Artefact Detection Challenge (EAD), we aim to identify hindrances like saturations, motion blur, specular reflections, bubbles, imaging artefacts, contrast and instrument using revolutionary techniques in artificial intelligence. This dataset has been updated with additional class instance (blood) and annotation samples. The provided challenge is important for endoscopy video processing task in obtaining more sensible temporal information. The challenge is sub-divided into three tasks: 1) Multi-class artefact detection: Localization of bounding boxes and class labels for 8 artefact classes for given frames 2) Region segmentation: Precise boundary delineation of detected artefacts for 5 artefact classes 3) Detection generalization: Detection performance independent of specific data type and source (test only) Useful tools for this dataset: https://sharibox.github.io/EAD2019/; https://github.com/sharibox/EndoCV2020 Updated version 2020: We have added an updated dataset of EAD2019 (https://data.mendeley.com/datasets/c7fjbxcgj9/2). Please note that several frames have been included in this dataset. If you use this dataset then please cite the listed works below: 1) Ali, S., Zhou, F., Daul, C., Braden, B., Bailey, A., Realdon, S., East, J., Wagni`eres, G., Loschenov, V., Grisan, E., et al., 2019. Endoscopy artifact detection (EAD 2019) challenge dataset. arXiv preprint arXiv:1905.03209 . 2) Ali, S., Zhou, F., Bailey, A., Braden, B., East, J.E., Lu, X., Rittscher, J., 2021. A deep learning framework for quality assessment and restoration in video endoscopy. Medical Image Analysis 68, 101900. doi:https://doi.org/10.1016/j.media.2020.101900. 3) Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R.D., et al., 2020. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific reports 10, 1–15. 4) Ali, S., Dmitrieva, M., Ghatwary, N. et al. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis, 2021. doi: https://doi.org/10.1016/j.media.2021.102002.



University of Oxford


Computer Vision, Biomedical Imaging, Deep Learning, Artifact Detection, Endoscopy, Segmentation