Deep Smoke Removal from Minimally Invasive Surgery Videos

Abstract

During video-guided minimally invasive surgery, quality of frames may be degraded severely by cauterization-induced smoke and condensation of vapor. This degradation of quality creates discomfort for the operating surgeon, and causes serious problems for automatic follow-up processes such as registration, segmentation and tracking. This paper proposes a novel deep neural network based smoke removal solution that is able to enhance the quality of surgery video frames in real-time. It employs synthetically generated training dataset including smoke embedded and clean reference versions. Results calculated on the test set indicate that our network outperforms previous defogging methods in terms of quantitative and qualitative measures. While eliminating apparent smoke, it also successfully preserves the natural appearance of tissue surface. To the best of our knowledge, the presented method is the first deep neural network based approach for the surgical field smoke removal problem.