International Journal of Frontiers in Engineering Technology, 2021, 3(10); doi: 10.25236/IJFET.2021.031009.
Zhengxuan Cao1,2, Zhengyi Zhong1,3, Hanze Wei1, Rongrong Liu1, Qun Wang1
1Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Science, Suzhou, China
2School of Electrical Engineering, Shandong University, Jinan, China
3School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
The visibility of the operating fields can be severely deteriorated by endoscopic smoke generated during laparoscopic surgery due to laser ablation and cauterization. Clinical studies have shown that removing smoke effects to laparoscopic images from the operating room reduces operating time and makes surgeons more comfortable during the procedure. Desmoking approaches based on deep learning have been found to be effective in the removal of laparoscopic smoke. This research will review several cutting-edge strategies for underlying theory and performance evaluations that have been developed in recent years.
Laparoscopic surgery, Smoke removal, Deep learning, Defogging, Supervised learning, Unsupervised learning
Zhengxuan Cao, Zhengyi Zhong, Hanze Wei, Rongrong Liu, Qun Wang. Recent Advances in Deep Learning-based Smoke Removal Techniques for Laparoscopic Images. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 10: 74-77. https://doi.org/10.25236/IJFET.2021.031009.
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