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.
Abstract (translated)
内窥镜伪影是促进中空器官疾病诊断和治疗的核心挑战。精确检测像素饱和度、运动模糊、镜面反射、气泡和碎片等特定伪影对于高质量的帧恢复至关重要，对于实现可靠的计算机辅助工具以改进患者护理至关重要。由于视频帧中存在大量的多类伪影，目前大多数内窥镜视频都没有进行分析。通过内窥镜伪影检测（EAD 2019）的挑战，我们通过解决内窥镜帧伪影的准确识别和定位来解决这一关键瓶颈问题，从而进一步对无法使用的视频帧进行关键的定量分析，如拼接和三维重建，这对于提高患者的C值至关重要。是。本文总结了挑战任务，描述了EAD 2019挑战中建立的数据集和评估标准。
URL
https://arxiv.org/abs/1905.03209
