Skip to main content

Advertisement

SpringerLink
Log in

Video-based surgical skill assessment using 3D convolutional neural networks

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

A profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills for minimally invasive or robot-assisted procedures. This includes the objective and preferably automatic assessment of surgical skill. Recent studies presented good results for automatic, objective skill evaluation by collecting and analyzing motion data such as trajectories of surgical instruments. However, obtaining the motion data generally requires additional equipment for instrument tracking or the availability of a robotic surgery system to capture kinematic data. In contrast, we investigate a method for automatic, objective skill assessment that requires video data only. This has the advantage that video can be collected effortlessly during minimally invasive and robot-assisted training scenarios.

Methods

Our method builds on recent advances in deep learning-based video classification. Specifically, we propose to use an inflated 3D ConvNet to classify snippets, i.e., stacks of a few consecutive frames, extracted from surgical video. The network is extended into a temporal segment network during training.

Results

We evaluate the method on the publicly available JIGSAWS dataset, which consists of recordings of basic robot-assisted surgery tasks performed on a dry lab bench-top model. Our approach achieves high skill classification accuracies ranging from 95.1 to 100.0%.

Conclusions

Our results demonstrate the feasibility of deep learning-based assessment of technical skill from surgical video. Notably, the 3D ConvNet is able to learn meaningful patterns directly from the data, alleviating the need for manual feature engineering. Further evaluation will require more annotated data for training and testing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://github.com/deepmind/kinetics-i3d/tree/master/data/checkpoints.

  2. https://github.com/yjxiong/tsn-pytorch.

  3. https://github.com/piergiaj/pytorch-i3d.

  4. http://yjxiong.me/others/kinetics_action.

References

  1. Ahmed K, Miskovic D, Darzi A, Athanasiou T, Hanna GB (2011) Observational tools for assessment of procedural skills: a systematic review. Am J Surg 202(4):469–480

    Article  PubMed  Google Scholar 

  2. Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Haro BB, Zappella L, Khudanpur S, Vidal R, Hager GD (2017) A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Trans Biomed Eng 64(9):2025–2041

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bouget D, Allan M, Stoyanov D, Jannin P (2017) Vision-based and marker-less surgical tool detection and tracking: a review of the literature. Med Image Anal 35:633–654

    Article  PubMed  Google Scholar 

  4. Bradski G (2000) The OpenCV library. Dr. Dobb’s J Softw Tools 25(11):120–125

    Google Scholar 

  5. Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R (1994) Signature verification using a “siamese” time delay neural network. In: NIPS, pp 737–744

  6. Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: CVPR, pp 4724–4733

  7. Chmarra MK, Grimbergen CA, Dankelman J (2007) Systems for tracking minimally invasive surgical instruments. Minim Invasive Ther Allied Technol 16(6):328–340

    Article  CAS  PubMed  Google Scholar 

  8. Doughty H, Damen D, Mayol-Cuevas WW (2018) Who’s better, who’s best: skill determination in video using deep ranking. In: CVPR, pp 6057–6066

  9. Du X, Kurmann T, Chang PL, Allan M, Ourselin S, Sznitman R, Kelly JD, Stoyanov D (2018) Articulated multi-instrument 2D pose estimation using fully convolutional networks. IEEE Trans Med Imaging 37(5):1276–1287

    Article  PubMed  Google Scholar 

  10. Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD (2018) Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int J Med Robot 14(1):e1850

    Article  Google Scholar 

  11. Gao Y, Vedula SS, Reiley CE, Ahmidi N, Varadarajan B, Lin HC, Tao L, Zappella L, Béjar B, Yuh DD, Chen CCG, Vidal R, Khudanpur S, Hager GD (2014) JHU-ISI gesture and skill assessment working set (JIGSAWS): a surgical activity dataset for human motion modeling. In: M2CAI

  12. Goh AC, Aghazadeh MA, Mercado MA, Hung AJ, Pan MM, Desai MM, Gill IS, Dunkin BJ (2015) Multi-institutional validation of fundamental inanimate robotic skills tasks. J Urol 194(6):1751–1756

    Article  PubMed  Google Scholar 

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778

  14. Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Evaluating surgical skills from kinematic data using convolutional neural networks. In: MICCAI, pp 214–221

  15. Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Article  PubMed  Google Scholar 

  16. Jin A, Yeung S, Jopling J, Krause J, Azagury D, Milstein A, Fei-Fei, L (2018) Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. In: WACV, pp 691–699

  17. Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, Suleyman M, Zisserman A (2017) The kinetics human action video dataset. arXiv preprint arXiv:1705.06950

  18. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: ICLR

  19. Laina I, Rieke N, Rupprecht C, Vizcaíno JP, Eslami A, Tombari F, Navab N (2017) Concurrent segmentation and localization for tracking of surgical instruments. In: MICCAI, pp 664–672

  20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  21. Martin J, Regehr G, Reznick R, Macrae H, Murnaghan J, Hutchison C, Brown M (1997) Objective structured assessment of technical skill (OSATS) for surgical residents. Br J Surg 84(2):273–278

    Article  CAS  PubMed  Google Scholar 

  22. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. In: NIPS Workshops

  23. Peters JH, Fried GM, Swanstrom LL, Soper NJ, Sillin LF, Schirmer B, Hoffman K, Sages FLS Committee (2004) Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery. Surgery 135(1):21–27

  24. Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: NIPS, pp 568–576

  25. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: CVPR, pp 2818–2826

  26. Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparse hidden markov models for surgical gesture classification and skill evaluation. In: IPCAI, pp 167–177

  27. Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: ICCV, pp 4489–4497

  28. Vedula SS, Ishii M, Hager GD (2017) Objective assessment of surgical technical skill and competency in the operating room. Annu Rev Biomed Eng 19:301–325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L (2016) Temporal segment networks: Towards good practices for deep action recognition. In: ECCV. Springer, pp 20–36

  30. Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L (2018) Temporal segment networks for action recognition in videos. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2018.2868668

  31. Wang Z, Majewicz Fey A (2018) Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int J Comput Assist Radiol Surg 13(12):1959–1970

    Article  PubMed  Google Scholar 

  32. Zach C, Pock T, Bischof H (2007) A duality based approach for realtime TV-L1 optical flow. In: Joint pattern recognition symposium. Springer, pp 214–223

  33. Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. Int J Comput Assist Radiol Surg 13(5):731–739

    Article  PubMed  Google Scholar 

  34. Zia A, Sharma Y, Bettadapura V, Sarin EL, Essa I (2018) Video and accelerometer-based motion analysis for automated surgical skills assessment. Int J Comput Assist Radiol Surg 13(3):443–455

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) for granting access to their GPU cluster for running additional experiments during paper revision.

Author information

Authors and Affiliations

  1. Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany

    Isabel Funke & Stefanie Speidel

  2. Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany

    Sören Torge Mees & Jürgen Weitz

Authors
  1. Isabel Funke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sören Torge Mees
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jürgen Weitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefanie Speidel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabel Funke.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

This articles does not contain patient data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Funke, I., Mees, S.T., Weitz, J. et al. Video-based surgical skill assessment using 3D convolutional neural networks. Int J CARS 14, 1217–1225 (2019). https://doi.org/10.1007/s11548-019-01995-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-019-01995-1

Keywords

Search

Navigation