[28] J. Han, J. Davids, H. Ashrafian, A. Darzi, D. S. Elson, and M. Sodergren. A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches. The International Journal of Medical Robotics and Computer Assisted Surgery, 18(2):e2358, 2022.
[29] D. Hearn, M. P. Baker, and M. P. Baker. Computer graphics with OpenGL, volume 3. Pearson Prentice Hall Upper Saddle River, NJ.: 2004.
[30] W.-Y. Hong, C.-L. Kao, Y.-H. Kuo, J.-R. Wang, W.-L. Chang, and C.-S. Shih. Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. arXiv preprint arXiv:2012.12453, 2020.
[31] Y. Hu, T. Schneider, B. Wang, D. Zorin, and D. Panozzo. Fast tetrahedral meshing in the wild. ACM Trans. Graph., 39(4), July 2020.
[32] F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021.
[33] R. Izzo, D. Steinman, S. Manini, and L. Antiga. The vascular modeling toolkit: a python library for the analysis of tubular structures in medical images. Journal of Open Source Software, 3(25):745, 2018.
[34] W. Jakob, S. Speierer, N. Roussel, and D. Vicini. Dr. JIT: a just-in-time compiler for differentiable rendering. ACM Transactions on Graphics (TOG), 41(4):1-19, 2022.
[35] Y. Ji, H. Bai, C. Ge, J. Yang, Y. Zhu, R. Zhang, Z. Li, L. Zhanng, W. Ma, X. Wan, et al. Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Advances in Neural Information Processing Systems, 35:36722–36732, 2022.
[36] H. Kenngott, J. Wünscher, M. Wagner, A. Preukschas, A. Wekerle, P. Neher, S. Suwelack, S. Speidel, F. Nickel, D. Oladokun, et al. Openhelp (heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool. Surgical endoscopy, 29:3338-3347, 2015.
[37] A. Khaddad, J.-C. Bernhard, G. Margue, C. Michiels, S. Ricard, K. Chandelier, F. Bladou, N. Bourdel, and A. Bartoli. A survey of augmented reality methods to guide minimally invasive partial nephrectomy. World Journal of Urology, 41(2):335-343, 2023.
[38] B. D. Killeen, S. M. Cho, M. Armand, R. H. Taylor, and M. Unberath. In silico simulation: A key enabling technology for next-generation intelligent surgical systems. Progress in Biomedical Engineering, 2023.
[39] H. U. Lemke and M. W. Vannier. The operating room and the need for an it infrastructure and standards. Int. J. Comput. Assist. Radiol. Surg., 1(3):117-121, 2006.
[40] S. Leonard, K. L. Wu, Y. Kim, A. Krieger, and P. C. Kim. Smart tissue anastomosis robot (star): A vision-guided robotics system for laparoscopic suturing. IEEE Transactions on Biomedical Engineering, 61(4):1305-1317, 2014.
[41] K. Li, M. I. Fathan, K. Patel, T. Zhang, C. Zhong, A. Bansal, A. Rastogi, J. S. Wang, and G. Wang. Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations. Plos one, 16(8):e0255809, 2021.
[42] L. Lin, Y. Liu, Y. Hu, X. Yan, K. Xie, and H. Huang. Capturing, reconstructing, and simulating: the urbanscene3d dataset. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part VIII, pages 93-109. Springer, 2022.
[43] X. Lin, Y. Wang, J. Olkin, and D. Held. Softgym: Benchmarking deep reinforcement learning for deformable object manipulation. In Conference on Robot Learning, pages 432-448. PMLR, 2021.
[44] W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987.