Abstract
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. We propose a fully automatic framework that can: 1) detect and classify six different primary artifacts, 2) provide a quality score for each frame and 3) restore mildly corrupted frames. To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. We introduce a quality metric to assess frame quality and predict image restoration success. Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. Our detector yields the highest mean average precision (mAP at 5% threshold) of 49.0 and the lowest computational time of 88 ms allowing for accurate real-time processing. Our restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos we show that our approach preserves an average of 68.7% which is 25% more frames than that retained from the raw videos.
Abstract (translated)
内窥镜检查是一种常规成像技术，用于诊断和微创外科治疗。运动模糊、气泡、镜面反射、浮动物体和像素饱和度等伪影妨碍了内窥镜视频的视觉解释和自动分析。考虑到内窥镜在不同临床应用中的广泛应用，我们认为，对此类伪影的可靠可靠可靠的识别和损坏视频帧的自动修复是一个基本的医学成像问题。现有的最先进的方法只处理所选工件的检测和恢复。然而，典型的内窥镜视频包含大量的人工制品，这些人工制品促使建立一个全面的解决方案。
URL
https://arxiv.org/abs/1904.07073
