Endoscopy artifact detection (EAD 2019) challenge dataset
This work addresses the bottleneck of analyzing endoscopic videos due to artifacts, benefiting medical professionals and patients by enabling reliable computer-assisted tools, though it is incremental as it focuses on dataset creation rather than a new method.
The paper tackled the problem of detecting artifacts in endoscopic videos, which hinder diagnosis and treatment, by introducing the EAD 2019 challenge dataset to enable accurate identification and localization of artifacts like pixel saturations and motion blur, facilitating tasks such as mosaicking and 3D reconstruction for improved patient care.
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.