The applications of Generative Adversarial Network in surgical videos

Abstract

Unstructured data e.g. images and videos are widely used in the medical field. Because the generative adversarial network (GAN) has the ability to process images with fewer labels and better feature extraction, the application of GAN in surgical video can promote the development of medical fields such as surgeon training and telemedicine. From three aspects, surgical procedure, video enhancement and imitation learning, the article summarizes the current applications of generative adversarial network (GAN) in surgical video processing. The first is two specific applications in terms of surgical procedure, step prediction (i.e. Supr-GAN) and surgical image generation. Second is about video enhancement. Based on the real-time performance and video processing effect, the paper introduces three types of applications in real-time video, respectively network delay, sharpness improvement and device recognition, and two processing methods to non-real-time video with the mirror reflection problem or the smoke problem. Finally, the paper also summarizes two applications of generative adversarial imitation learning (GAIL) in surgical videos, which focus on surgical suture (i.e. Motion2Vec) or selective catheterization simulation. The summary indicate that GAN currently focuses on minimally invasive surgery video processing, which means the type of surgery is relatively monotony. In addition, GAIL is seldom used to simulate learning based on surgery videos. Therefore, GAN, especially GAIL, still has a broad prospect in the application of surgical videos.