Zhao PT, Leavitt DA, Richstone L, Kavoussi LR (2017) Laparoscopic partial nephrectomy. Manag Small Ren Masses Diagn Manag 17:95–106
Marszalek M, Meixl H, Polajnar M, Rauchenwald M, Jeschke K, Madersbacher S (2009) Laparoscopic and open partial nephrectomy: a matched-pair comparison of 200 patients. Eur Urol 55(5):1171–1178
Lane BR, Campbell SC, Gill IS (2013) 10-year oncologic outcomes after laparoscopic and open partial nephrectomy. The Journal of urology 190(1):44–49
Long Y, Li Z, Yee CH, Ng CF, Taylor RH, Unberath M, Dou Q (2021) E-dssr: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Proceedings of MICCAI 2021, Part IV 24, pp. 415–425. Springer
Robu MR, Ramalhinho J, Thompson S, Gurusamy K, Davidson B, Hawkes D, Stoyanov D, Clarkson MJ (2018) Global rigid registration of ct to video in laparoscopic liver surgery. International Journal of Computer Assisted Radiology and Surgery 13:947–956
Article PubMed PubMed Central Google Scholar
Guan P, Luo H, Guo J, Zhang Y, Jia F (2023) Intraoperative laparoscopic liver surface registration with preoperative ct using mixing features and overlapping region masks. Int J Comp Assist Radiol Surg 18:1–11
Yang Z, Simon R, Linte CA (2023) Learning feature descriptors for pre-and intra-operative point cloud matching for laparoscopic liver registration. Int J Comp Assist Radiol Surg 18:1–8
Zhang X, Wang J, Wang T, Ji X, Shen Y, Sun Z, Zhang X (2019) A markerless automatic deformable registration framework for augmented reality navigation of laparoscopy partial nephrectomy. Int J Comput Assist Radiol Surg 14:1285–1294
Luo H, Yin D, Zhang S, Xiao D, He B, Meng F, Zhang Y, Cai W, He S, Zhang W et al (2020) Augmented reality navigation for liver resection with a stereoscopic laparoscope. Comput Methods Programs Biomed 187:105099
Visentini-Scarzanella M, Sugiura T, Kaneko T, Koto S (2017) Deep monocular 3d reconstruction for assisted navigation in bronchoscopy. Int J Comput Assist Radiol Surg 12(7):1089–1099
Huang B, Zheng J-Q, Nguyen A, Tuch D, Vyas K, Giannarou S, Elson DS (2021) Self-supervised generative adversarial network for depth estimation in laparoscopic images. In: Proceedings of MICCAI 2021, Part IV 24, pp. 227–237. Springer
Tukra S, Giannarou S (2022) Randomly connected neural networks for self-supervised monocular depth estimation. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization 10(4):390–399
Edwards PE, Psychogyios D, Speidel S, Maier-Hein L, Stoyanov D (2022) Serv-ct: A disparity dataset from cone-beam ct for validation of endoscopic 3d reconstruction. Med Image Anal 76:102302
Article PubMed PubMed Central Google Scholar
Cheng X, Zhong Y, Harandi M, Drummond T, Wang Z, Ge Z (2022) Deep laparoscopic stereo matching with transformers. In: Proceedings of MICCAI 2022, Part VII, pp. 464–474. Springer
Xu C, Huang B, Elson DS (2022) Self-supervised monocular depth estimation with 3-d displacement module for laparoscopic images. IEEE transactions on medical robotics and bionics 4(2):331–334
Article PubMed PubMed Central Google Scholar
Bardozzo F, Collins T, Forgione A, Hostettler A, Tagliaferri R (2022) Stasis-net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3d laparoscopy. Med Image Anal 77:102380
Huang B, Zheng J-Q, Nguyen A, Xu C, Gkouzionis I, Vyas K, Tuch D, Giannarou S, Elson DS (2022) Self-supervised depth estimation in laparoscopic image using 3d geometric consistency. In: Proceedings of MICCAI 2022, Part VII, pp. 13–22. Springer
Tao R, Huang B, Zou X, Zheng G (2023) SVT-SDE: Spatiotemporal vision transformers based self-supervised depth estimation in stereoscopic surgical videos. IEEE Trans Med Robot Bionics 5(1):42–53
Chen Z, Cruciani L, Lievore E, Fontana M, De Cobelli O, Musi G, Ferrigno G, De Momi E (2024) Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training. Comput Methods Programs Biomed 244:107937
Wu R, Liang P, Liu Y, Huang Y, Li W, Chang Q (2025) Laparoscopic stereo matching using 3-dimensional fourier transform with full multi-scale features. Eng Appl Artif Intell 139:109654
Wang X, Yang B, Wei M, Liu L, Zhang J, Nie Y (2025) Deep learning for endoscopic depth estimation: a review. Displays 90:1–12, Article 103086. https://doi.org/10.1016/j.displa.2025.103086
Yang Z, Simon R, Linte CA (2023) Disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images. Journal of Medical Imaging 10(4):045001–045001
Article PubMed PubMed Central Google Scholar
Ye M, Johns E, Handa A, Zhang L, Pratt P, Yang G-Z (2017) Self-supervised siamese learning on stereo image pairs for depth estimation in robotic surgery. In: The Hamlyn Symposium on Medical Robotics, p. 27
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of CVPR 2021, pp. 10012–10022
Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2022) Video swin transformer. In: Proceedings of CVPR 2022, pp. 3202–3211
Hore A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. In: Proceedings of ICPR 2010, pp. 2366–2369. IEEE
Talebi H, Milanfar P (2018) Learned perceptual image enhancement. In: 2018 IEEE International Conference on Computational Photography (ICCP), pp. 1–13. IEEE
Abdi H, Williams LJ (2010) Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2(4):433–459
Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, 1611, pp. 586–606. Spie
Myronenko A, Song X (2010) Point set registration: Coherent point drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275
Zhang X, Wang T, Zhang X, Zhang Y, Wang J (2020) Assessment and application of the coherent point drift algorithm to augmented reality surgical navigation for laparoscopic partial nephrectomy. Int J Comput Assist Radiol Surg 15(6):989–999
Tukra S, Giannarou S (2022) Stereo depth estimation via self-supervised contrastive representation learning. In: Proceedings of MICCAI 2022, Part VII, pp. 604–614. Springer
Yang Z, Simon R, Li Y, Linte CA (2021) Dense depth estimation from stereo endoscopy videos using unsupervised optical flow methods. In: Proceedings of MIUA 2021, pp. 337–349. Springer
Conover WJ (1973) On methods of handling ties in the wilcoxon signed-rank test. Journal of the American Statistical Association 68(344):985–988
Cui B, Islam M, Bai L, Ren H (2024) Surgical-dino: adapter learning of foundation models for depth estimation in endoscopic surgery. Int J Comput Assist Radiol Surg 19(6):1013–1020
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