Endoscopy artifact detection (EAD 2019) challenge dataset

TL;DR

The paper introduces the EAD 2019 challenge dataset aimed at improving the detection of artifacts in endoscopic videos to enhance diagnostic accuracy and facilitate advanced image analysis techniques.

Contribution

It provides a new dataset and evaluation framework for the accurate detection and localization of endoscopic artifacts, addressing a key bottleneck in medical video analysis.

Findings

Established benchmark for artifact detection in endoscopy

Facilitates development of reliable artifact localization methods

Supports improved video analysis for patient care

Abstract

Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realizing reliable computer-assisted tools for improved patient care. At present most videos in endoscopy are currently not analyzed due to the abundant presence of multi-class artifacts in video frames. Through the endoscopic artifact detection (EAD 2019) challenge, we address this key bottleneck problem by solving the accurate identification and localization of endoscopic frame artifacts to enable further key quantitative analysis of unusable video frames such as mosaicking and 3D reconstruction which is crucial for delivering improved patient care. This paper summarizes the challenge tasks and describes the dataset and evaluation criteria established in the EAD 2019 challenge.