Dsmk-DcSeg-Lap, a Generative Adversarial Network Guided by Dark-Chanel and Segmentation to Smoke Removal in Laparoscopic Images

In this chapter, a computational approach is proposed to address the challenge of degraded visibility caused by smoke during laparoscopic surgery. The visualization of organs and tissues is hindered by the presence of smoke, which results from dissection tools. This, in turn, leads to potential errors and increased surgical duration, ultimately impacting patient outcomes. To overcome this issue, a novel neural architecture is introduced, which consists of two autoencoders trained using the generative neural network paradigm. The image segmentation on the laparoscopic image is performed by the first autoencoder, while the second autoencoder incorporates this segmented image as an additional fifth channel. To evaluate the effectiveness of the approach, comprehensive quantitative assessments are conducted, and the results are compared with state-of-the-art desmoking and dehazing techniques. Performance evaluation is carried out using commonly used metrics in the field. The superiority of the proposed method over existing approaches is demonstrated by the obtained results. This makes the method highly suitable for integration into medical systems using embedded devices.