Removal of smoke effects in laparoscopic surgery via adversarial neural network and the dark channel prior

Abstract

Background: In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools that reduce the visibility of the observed organs and tissues. Objective: Improve visibility in laparoscopic surgery by combining image processing techniques based on classical methods and artificial intelligence. Method: Development of a hybrid approach to eliminating the effects of surgical smoke, based on the combination of the dark channel prior (DCP) method and a pixel-to-pixel neural network architecture known as a generative adversarial network (GAN). Results: Experimental results have shown that the proposed method achieves better performance than individual DCP and GAN results in terms of restoration quality, obtaining (according to PSNR and SSIM index metrics) better results than some related state-of-the-art methods. Conclusions: The proposed approach decreases the risks and time of laparoscopic surgery because once the network is trained, the system can improve real-time visibility.