Skip to main content
SpringerLink
Log in

ISINet: An Instance-Based Approach for Surgical Instrument Segmentation

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12263))

Abstract

We study the task of semantic segmentation of surgical instruments in robotic-assisted surgery scenes. We propose the Instance-based Surgical Instrument Segmentation Network (ISINet), a method that addresses this task from an instance-based segmentation perspective. Our method includes a temporal consistency module that takes into account the previously overlooked and inherent temporal information of the problem. We validate our approach on the existing benchmark for the task, the Endoscopic Vision 2017 Robotic Instrument Segmentation Dataset [2], and on the 2018 version of the dataset [1], whose annotations we extended for the fine-grained version of instrument segmentation. Our results show that ISINet significantly outperforms state-of-the-art methods, with our baseline version duplicating the Intersection over Union (IoU) of previous methods and our complete model triplicating the IoU.

C. González and L. Bravo-Sánchez—Both authors contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/BCV-Uniandes/ISINet.

References

  1. Allan, M., et al.: 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190 (2020)

  2. Allan, M., et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)

  3. Bodenstedt, S., et al.: Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery. arXiv preprint arXiv:1805.02475 (2018)

  4. Bouget, D., Benenson, R., Omran, M., Riffaud, L., Schiele, B., Jannin, P.: Detecting surgical tools by modelling local appearance and global shape. IEEE Trans. Med. Imaging 34(12), 2603–2617 (2015)

    Article  Google Scholar 

  5. Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611–625. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3_44

    Chapter  Google Scholar 

  6. Du, X., et al.: Articulated multi-instrument 2-D pose estimation using fully convolutional networks. IEEE Trans. Med. Imaging 37(5), 1276–1287 (2018). https://doi.org/10.1109/tmi.2017.2787672

    Article  Google Scholar 

  7. Lee, E.J., Plishker, W., Liu, X., Kane, T., Bhattacharyya, S.S., Shekhar, R.: Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning-based framework, vol. 10951 (2019). https://doi.org/10.1117/12.2512994

  8. García-Peraza-Herrera, L.C., et al.: ToolNet: holistically-nested real-time segmentation of robotic surgical tools. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5717–5722. IEEE (2017)

    Google Scholar 

  9. García-Peraza-Herrera, L.C., et al.: Real-time segmentation of non-rigid surgical tools based on deep learning and tracking. In: Peters, T., et al. (eds.) CARE 2016. LNCS, vol. 10170, pp. 84–95. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54057-3_8

    Chapter  Google Scholar 

  10. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: The IEEE International Conference on Computer Vision (ICCV), October 2017

    Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  12. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17

  13. Islam, M., Li, Y., Ren, H.: Learning where to look while tracking instruments in robot-assisted surgery. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 412–420. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_46

    Chapter  Google Scholar 

  14. Jin, Y., Cheng, K., Dou, Q., Heng, P.-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 440–448. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_49

    Chapter  Google Scholar 

  15. Joskowicz, L.: Computer-aided surgery meets predictive, preventive, and personalized medicine. EPMA J. 8(1), 1–4 (2017)

    Article  Google Scholar 

  16. Jung, I., Son, J., Baek, M., Han, B.: Real-time MDNet. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 89–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_6

    Chapter  Google Scholar 

  17. Kletz, S., Schoeffmann, K., Benois-Pineau, J., Husslein, H.: Identifying surgical instruments in laparoscopy using deep learning instance segmentation. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–6 (2019)

    Google Scholar 

  18. Kurmann, T., et al.: Simultaneous recognition and pose estimation of instruments in minimally invasive surgery. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 505–513. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_57

    Chapter  Google Scholar 

  19. Lalys, F., Jannin, P.: Surgical process modelling: a review. Int. J. Comput. Assist. Radiol. Surg. 9(3), 495–511 (2013)

    Article  Google Scholar 

  20. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  21. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  22. Massa, F., Girshick, R.: MaskRCNN-benchmark: fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch (2018). https://github.com/facebookresearch/maskrcnn-benchmark

  23. Mohammed, A., Yildirim, S., Farup, I., Pedersen, M., Hovde, Ø.: Streoscennet: surgical stereo robotic scene segmentation, vol. 10951 (2019). https://doi.org/10.1117/12.2512518

  24. Reda, F., Pottorff, R., Barker, J., Catanzaro, B.: flownet2-pytorch: pytorch implementation of flownet 2.0: evolution of optical flow estimation with deep networks (2017). https://github.com/NVIDIA/flownet2-pytorch

  25. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  26. Ross, T., et al.: Robust medical instrument segmentation challenge 2019. arXiv preprint arXiv:2003.10299 (2020)

  27. Shvets, A.A., Rakhlin, A., Kalinin, A.A., Iglovikov, V.I.: Automatic instrument segmentation in robot-assisted surgery using deep learning (2018)

    Google Scholar 

  28. Spediel, S., et al.: Surgical workflow and skill analysis (2019). https://endovissub-workflowandskill.grand-challenge.org

  29. Intuitive Surgical: Da vinci surgical system (2019). https://www.intuitive.com/en-us/products-and-services/da-vinci

  30. Sznitman, R., Ali, K., Richa, R., Taylor, R.H., Hager, G.D., Fua, P.: Data-driven visual tracking in retinal microsurgery. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 568–575. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_70

    Chapter  Google Scholar 

  31. Sznitman, R., Becker, C., Fua, P.: Fast part-based classification for instrument detection in minimally invasive surgery. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 692–699. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_86

    Chapter  Google Scholar 

  32. Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86–97 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Dr. Germán Rosero for his support in the verification of the instrument type annotations.

Author information

Authors and Affiliations

  1. Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia

    Cristina González, Laura Bravo-Sánchez & Pablo Arbelaez

Authors
  1. Cristina González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Bravo-Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pablo Arbelaez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina González .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2049 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

González, C., Bravo-Sánchez, L., Arbelaez, P. (2020). ISINet: An Instance-Based Approach for Surgical Instrument Segmentation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59716-0_57

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59715-3

  • Online ISBN: 978-3-030-59716-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation