Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN

Vishal Venkatesh, Neeraj Sharma, Vivek Srivastava, Munendra Singh

Research output: Contribution to journalArticlepeer-review

Abstract

In laparoscopic surgery, energized dissecting devices and laser ablation causes smoke, which degrades the visual quality of the operative field. This paper proposes an unsupervised approach to desmoke laparoscopic images called Cyclic-DesmokeGAN. In the generator, multi-scale residual blocks help to alleviate the smoke component at multiple scales, while refinement module helps to obtain desmoked images with sharper boundaries. As the presence of smoke degrades contrast and fine structure, the proposed method utilizes high boost filtered image at each encoder layer. The contrast loss improves overall contrast, thereby reducing the smoke, while Unsharp Regularization loss helps to stabilize the network. The proposed Cyclic-DesmokeGAN is tested on 200 smoke images obtained from Cholec80 dataset consisting of videos of cholecystectomy surgeries. The results depict effectiveness, as proposed approach achieved 3.47±0.09 Contrast-Distorted Images Quality, 4.15±0.74 Naturalness Image Quality Evaluator, and 0.23±0.00 Fog Aware Density Evaluator, these indexes are best in comparison to other state-of-the-art methods.

Original languageEnglish
Article number103873
JournalComputers in Biology and Medicine
Volume123
DOIs
Publication statusPublished - 08-2020

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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