Skip to main content

Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data

  • Conference paper
  • First Online:
Simulation and Synthesis in Medical Imaging (SASHIMI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15187))

Included in the following conference series:

  • 170 Accesses

Abstract

Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is compromised by image distortions common in surgical settings. To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models. We have compiled a comprehensive dataset that reflects real-world conditions, incorporating a range of synthetically induced corruptions at varying severity levels. To further this effort, we introduce the Depth Estimation Robustness Score (DERS), a novel metric that combines measures of error, accuracy, and robustness to meet the multifaceted requirements of surgical applications. This metric acts as a foundational element for evaluating performance, establishing a new paradigm for the comparative analysis of depth estimation technologies. Additionally, we set forth a benchmark focused on robustness for the evaluation of depth estimation in endoscopic surgery, with the aim of driving progress in model refinement. A thorough analysis of two monocular depth estimation models using our framework reveals crucial information about their reliability under adverse conditions. Our results emphasize the essential need for algorithms that can tolerate data corruption, thereby advancing discussions on improving model robustness. The impact of this research transcends theoretical frameworks, providing concrete gains in surgical precision and patient safety. This study establishes a benchmark for the robustness of depth estimation and serves as a foundation for developing more resilient surgical support technologies. Code is available at https://github.com/lofrienger/EndoDepthBenchmark.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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://endovissub2019-scared.grand-challenge.org.

  2. 2.

    https://github.com/bethgelab/imagecorruptions.

References

  1. Allan, M., Mcleod, J., Wang, C., Rosenthal, J.C., Hu, Z., Gard, N., Eisert, P., Fu, K.X., Zeffiro, T., Xia, W., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021)

  2. Bogdanova, R., Boulanger, P., Zheng, B.: Depth perception of surgeons in minimally invasive surgery. Surgical innovation 23(5), 515–524 (2016)

    Article  Google Scholar 

  3. Breedveld, P., Stassen, H., Meijer, D., Stassen, L.: Theoretical background and conceptual solution for depth perception and eye-hand coordination problems in laparoscopic surgery. Minimally invasive therapy & allied technologies 8(4), 227–234 (1999)

    Article  Google Scholar 

  4. Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3828–3838 (2019)

    Google Scholar 

  5. Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations (2019)

    Google Scholar 

  6. Hofmeister, J., Frank, T.G., Cuschieri, A., Wade, N.J.: Perceptual aspects of two-dimensional and stereoscopic display techniques in endoscopic surgery: review and current problems. In: Seminars in laparoscopic surgery. vol. 8, pp. 12–24. Sage Publications Sage CA: Thousand Oaks, CA (2001)

    Google Scholar 

  7. Kong, L., Liu, Y., Li, X., Chen, R., Zhang, W., Ren, J., Pan, L., Chen, K., Liu, Z.: Robo3d: Towards robust and reliable 3d perception against corruptions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19994–20006 (2023)

    Google Scholar 

  8. Liu, X., Sinha, A., Ishii, M., Hager, G.D., Reiter, A., Taylor, R.H., Unberath, M.: Dense depth estimation in monocular endoscopy with self-supervised learning methods. IEEE transactions on medical imaging 39(5), 1438–1447 (2019)

    Article  Google Scholar 

  9. Liu, X., Sinha, A., Unberath, M., Ishii, M., Hager, G.D., Taylor, R.H., Reiter, A.: Self-supervised learning for dense depth estimation in monocular endoscopy. In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. pp. 128–138. Springer International Publishing, Cham (2018)

    Google Scholar 

  10. Mancini, M., Costante, G., Valigi, P., Ciarfuglia, T.A.: Fast robust monocular depth estimation for obstacle detection with fully convolutional networks. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4296–4303. IEEE (2016)

    Google Scholar 

  11. Ozyoruk, K.B., Gokceler, G.I., Bobrow, T.L., Coskun, G., Incetan, K., Almalioglu, Y., Mahmood, F., Curto, E., Perdigoto, L., Oliveira, M., et al.: Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Medical image analysis 71, 102058 (2021)

    Article  Google Scholar 

  12. Shao, S., Pei, Z., Chen, W., Zhang, B., Wu, X., Sun, D., Doermann, D.: Self-supervised learning for monocular depth estimation on minimally invasive surgery scenes. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 7159–7165. IEEE (2021)

    Google Scholar 

  13. Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., Sun, D., Zhang, B.: Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue. Medical image analysis 77, 102338 (2022)

    Article  Google Scholar 

  14. Tomazic, P.V., Sommer, F., Treccosti, A., Briner, H.R., Leunig, A.: 3d endoscopy shows enhanced anatomical details and depth perception vs 2d: a multicentre study. European Archives of Oto-Rhino-Laryngology 278, 2321–2326 (2021)

    Article  Google Scholar 

  15. Wang, A., Islam, M., Xu, M., Ren, H.: Curriculum-based augmented fourier domain adaptation for robust medical image segmentation. IEEE Transactions on Automation Science and Engineering (2023). 10.1109/TASE.2023.3295600

    Article  Google Scholar 

  16. Wang, R., Geng, Z., Zhang, Z., Pei, R., Meng, X.: Autostereoscopic augmented reality visualization for depth perception in endoscopic surgery. Displays 48, 50–60 (2017)

    Article  Google Scholar 

  17. Widya, A.R., Monno, Y., Okutomi, M., Suzuki, S., Gotoda, T., Miki, K.: Self-supervised monocular depth estimation in gastroendoscopy using gan-augmented images. In: Medical Imaging 2021: Image Processing. vol. 11596, pp. 319–328. SPIE (2021)

    Google Scholar 

  18. Xian, K., Cao, Z., Shen, C., Lin, G.: Towards robust monocular depth estimation: A new baseline and benchmark. International Journal of Computer Vision pp. 1–19 (2024)

    Google Scholar 

  19. Yang, Z., Pan, J., Dai, J., Sun, Z., Xiao, Y.: Self-supervised lightweight depth estimation in endoscopy combining cnn and transformer. IEEE Transactions on Medical Imaging (2024)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Hong Kong RGC CRF C4026-21G, GRF 14211420 & 14203323; Shenzhen-Hong Kong-Macau Technology Research Programme (Type C) STIC Grant SGDX20210823103535014 (202108233000303).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongliang Ren .

Editor information

Editors and Affiliations

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare.

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 79 KB)

A Appendix

A Appendix

See Tables 2 and 3.

Table 2. Quantitative results of MonoDepth2. Severity 0 corresponds to results on uncorrupted data. DERS are reported following respective data corruption.
Table 3. Quantitative results of AF-SfMLearner. Severity 0 corresponds to results on uncorrupted data. DERS are reported following respective data corruption.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, A., Yin, H., Cui, B., Xu, M., Ren, H. (2025). Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data. In: Fernandez, V., Wolterink, J.M., Wiesner, D., Remedios, S., Zuo, L., Casamitjana, A. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2024. Lecture Notes in Computer Science, vol 15187. Springer, Cham. https://doi.org/10.1007/978-3-031-73281-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73281-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73280-5

  • Online ISBN: 978-3-031-73281-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics