Advertisement

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

  • Isabel FunkeEmail author
  • Sören Torge Mees
  • Jürgen Weitz
  • Stefanie Speidel
Original Article

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.

Keywords

Surgical skill assessment Objective skill evaluation Technical surgical skill Surgical motion 3D convolutional neural network Temporal segment network Deep learning 

Notes

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.

Compliance with ethical standards

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.

References

  1. 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):469–480CrossRefGoogle Scholar
  2. 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):2025–2041CrossRefGoogle Scholar
  3. 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 35:633–654CrossRefGoogle Scholar
  4. 4.
    Bradski G (2000) The OpenCV library. Dr. Dobb’s J Softw Tools 25(11):120–125Google Scholar
  5. 5.
    Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R (1994) Signature verification using a “siamese” time delay neural network. In: NIPS, pp 737–744Google Scholar
  6. 6.
    Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: CVPR, pp 4724–4733Google Scholar
  7. 7.
    Chmarra MK, Grimbergen CA, Dankelman J (2007) Systems for tracking minimally invasive surgical instruments. Minim Invasive Ther Allied Technol 16(6):328–340CrossRefGoogle Scholar
  8. 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 6057–6066Google Scholar
  9. 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):1276–1287CrossRefGoogle Scholar
  10. 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):e1850CrossRefGoogle Scholar
  11. 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: M2CAIGoogle Scholar
  12. 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):1751–1756CrossRefGoogle Scholar
  13. 13.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778Google Scholar
  14. 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 214–221Google Scholar
  15. 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):221–231CrossRefGoogle Scholar
  16. 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 691–699Google Scholar
  17. 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. 18.
    Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: ICLRGoogle Scholar
  19. 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 664–672Google Scholar
  20. 20.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  21. 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):273–278CrossRefGoogle Scholar
  22. 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 WorkshopsGoogle Scholar
  23. 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):21–27Google Scholar
  24. 24.
    Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: NIPS, pp 568–576Google Scholar
  25. 25.
    Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: CVPR, pp 2818–2826Google Scholar
  26. 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 167–177Google Scholar
  27. 27.
    Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: ICCV, pp 4489–4497Google Scholar
  28. 28.
    Vedula SS, Ishii M, Hager GD (2017) Objective assessment of surgical technical skill and competency in the operating room. Annu Rev Biomed Eng 19:301–325CrossRefGoogle Scholar
  29. 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 20–36Google Scholar
  30. 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. 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):1959–1970CrossRefGoogle Scholar
  32. 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 214–223Google Scholar
  33. 33.
    Zia A, Essa I (2018) Automated surgical skill assessment in RMIS training. Int J Comput Assist Radiol Surg 13(5):731–739CrossRefGoogle Scholar
  34. 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):443–455CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Division of Translational Surgical OncologyNational Center for Tumor Diseases (NCT), Partner Site DresdenDresdenGermany
  2. 2.Department of Visceral, Thoracic and Vascular SurgeryFaculty of Medicine and University Hospital Carl Gustav CarusTU DresdenGermany

Personalised recommendations