327
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments

, , , , , , & show all
Pages 225-232 | Received 30 Sep 2020, Accepted 07 Oct 2020, Published online: 10 Nov 2020

References

  • Alaker M, Wynn GR, Arulampalam T. 2016. Virtual reality training in laparoscopic surgery: A systematic review & meta-analysis. Int J Surg. 29:85–94. doi:10.1016/j.ijsu.2016.03.034.
  • Fuentes-Hurtado F, Kadkhodamohammadi A, Flouty E, Barbarisi S, Luengo I, Stoyanov D. 2019. EasyLabels: weak labels for scene segmentation in laparoscopic videos. Int J Comput Assist Radiol Surg. 14(7):1247–1257. doi:10.1007/s11548-019-02003-2.
  • Funke I, Mees ST, Weitz J, Speidel S. 2019. Video-based surgical skill assessment using 3D convolutional neural networks. Int J Comput Assist Radiol Surg. 14(7):1217–1225. doi:10.1007/s11548-019-01995-1.
  • García-Peraza-Herrera LC, Li W, Fidon L, Gruijthuijsen C, Devreker A, Attilakos G, Deprest J, Poorten EBV, Stoyanov D, Vercauteren T, et al. 2017. ToolNet: holistically-nested real-time segmentation of robotic surgical tools. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada. p. 5717–5722.
  • Gibson E, Robu MR, Thompson S, Edwards P, Schneider C, Gurusamy K, Davidson B, Hawkes DJ, Barratt DC, Clarkson MJ. 2017. Deep residual networks for automatic segmentation of laparoscopic videos of the liver. In: Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, Proceedings of SPIE, Orlando, FL. vol. 10135, p. 101351M.
  • Nicolau S, Soler L, Mutter D, Marescaux J. 2011. Augmented reality in laparoscopic surgical oncology. Surg Oncol. 20:189–201. doi:10.1016/j.suronc.2011.07.002.
  • Nwoye CI, Mutter D, Marescaux J, Padoy N. 2019. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. Int J Comput Assist Radiol Surg. 14(6):1059–1067. doi:10.1007/s11548-019-01958-6.
  • Pfeiffer M, Funke I, Robu MR, Bodenstedt S, Strenger L, Engelhardt S, Roß T, Clarkson MJ, Gurusamy K, Davidson BR, et al. 2019. Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors. Medical image computing and computer assisted intervention - MICCAI 2019 part V. LNCS (Vol. 11768). Cham: Springer; p. 119–127.
  • Ronneberger O, Fischer P, Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical image computing and computer-assisted intervention - MICCAI 2015 part III. LNCS (Vol. 9351). Cham: Springer; p. 234–241.
  • Ros G, Sellart L, Materzynska J, Vazquez D, Lopez AM. 2016. The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV. p. 3234–3243.
  • Roth HR, Oda M, Shimizu M, Oda H, Hayashi Y, Kitasaka T, Fujiwara M, Misawa K, Mori K. 2018. Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. In: Medical Imaging 2018: Image Processing, Proceedings of SPIE, Houston, TX. vol. 10574, p. 105740B.
  • Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N. 2017. EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging. 36(1):86–97. doi:10.1109/TMI.2016.2593957.
  • Zhu J-Y, Park T, Isola P, Efros AA. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE International Conference on Computer Vision (ICCV), Venice, Italy. p. 2223–2232.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.