Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet

V. Vishal, Varun Venkatesh, Kshetrimayum Lochan, Neeraj Sharma, Munendra Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The presence of surgical smoke in laparoscopic surgery reduces the visibility of the operative field. In order to ensure better visualization, the present paper proposes an unsupervised deep learning approach for the task of desmoking of the laparoscopic images. This network builds upon generative adversarial networks (GANs) and converts laparoscopic images from smoke domain to smoke-free domain. The network comprises a new generator architecture that has an encoder-decoder structure composed of multi-scale feature extraction (MSFE) blocks at each encoder block. The MSFE blocks of the generator capture features at multiple scales to obtain a robust deep representation map and help to reduce the smoke component in the image. Further, a structure-consistency loss has been introduced to preserve the structure in the desmoked images. The proposed network is called Multi-scale DesmokeNet, which has been evaluated on the laparoscopic images obtain from Cholec80dataset. The quantitative and qualitative results shows the efficacy of the proposed Multi-scale DesmokeNet in comparison with other state-of-the-art desmoking methods.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 20th International Conference, ACIVS 2020, Proceedings
EditorsJacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders
PublisherSpringer Gabler
Pages421-432
Number of pages12
ISBN (Print)9783030406042
DOIs
Publication statusPublished - 2020
Event20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020 - Auckland, New Zealand
Duration: 10-02-202014-02-2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12002 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020
CountryNew Zealand
CityAuckland
Period10-02-2014-02-20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Vishal, V., Venkatesh, V., Lochan, K., Sharma, N., & Singh, M. (2020). Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet. In J. Blanc-Talon, P. Delmas, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems - 20th International Conference, ACIVS 2020, Proceedings (pp. 421-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12002 LNCS). Springer Gabler. https://doi.org/10.1007/978-3-030-40605-9_36