Endoscopy Artefact Detection (EAD) Dataset

Published: 1 March 2019| Version 1 | DOI: 10.17632/c7fjbxcgj9.1
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Description

We aim to establish a first large and comprehensive dataset for "Endoscopy artefact detection". The provided data is obtained from 6 different data centres that includes John Radcliffe Hospital, Oxford, UK; ICL Cancer Institute, Nancy, France; Ambroise Paré Hospital of Boulogne-Billancourt, Paris, France; Istituto Oncologico Veneto, Padova, Italy; University Hospital Vaudois, Lausanne, Switzerland; Botkin Clinical City Hospital, Moscow. This dataset is unique and represent multi-tissue (gastroscopy, cystoscopy, gastro-oesophageal, colonoscopy), multi-modal (white light, fluorescence, and narrow band imaging), inter patient and multi-population (UK, France, Russia, and Switzerland) endoscopic video frames. Videos were collected from patients on a first-come-first-served basis at Oxford, while randomized sampling was done at French centres and only cancer patients were chosen at the Moscow centre. Videos at these centres were acquired with standard imaging protocols using endoscopes built by different companies like Olympus, Biospec, and Karl Storz. While building our dataset, we have randomly mixed these data with no exclusion criteria. All images has been anonymised carefully and thereby no patient information is expected to be visible in this data. The released data is a part of Endoscopy Artefact Detection (EAD2019) IEEE ISBI'19 challenge. With the Endoscopy Artefact Detection Challenge (EAD2019), we aim to identify hindrances like saturations, motion blur, specular reflections, bubbles, imaging artefacts, contrast and instrument using revolutionary techniques in artificial intelligence. This step is vital in obtaining more sensible temporal information that can motivate and help both endoscopists and image analysts to develop more comprehensive computer-assisted tools. The challenge is sub-divided into three tasks: 1) Multi-class artefact detection: Localization of bounding boxes and class labels for 7 artefact classes for given frames 2) Region segmentation: Precise boundary delineation of detected artefacts 3) Detection generalization: Detection performance independent of specific data type and source Details of this challenge can be found at: https://ead2019.grand-challenge.org Useful tools for this dataset: https://sharibox.github.io/EAD2019/

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Artifact Detection, Endoscopy, Segmentation

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