TY - GEN
T1 - Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet
AU - Vishal, V.
AU - Venkatesh, Varun
AU - Lochan, Kshetrimayum
AU - Sharma, Neeraj
AU - Singh, Munendra
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85080858580
UR - https://www.scopus.com/pages/publications/85080858580#tab=citedBy
U2 - 10.1007/978-3-030-40605-9_36
DO - 10.1007/978-3-030-40605-9_36
M3 - Conference contribution
AN - SCOPUS:85080858580
SN - 9783030406042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 421
EP - 432
BT - Advanced Concepts for Intelligent Vision Systems - 20th International Conference, ACIVS 2020, Proceedings
A2 - Blanc-Talon, Jacques
A2 - Delmas, Patrice
A2 - Philips, Wilfried
A2 - Popescu, Dan
A2 - Scheunders, Paul
PB - Springer Gabler
T2 - 20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020
Y2 - 10 February 2020 through 14 February 2020
ER -