Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. CA: A Cancer J Clin 73(1):17–48

Google Scholar 

Saji H, Okada M, Tsuboi M, Nakajima R, Suzuki K, Aokage K, Aoki T, Okami J, Yoshino I, Ito H, Okumura N, Yamaguchi M, Ikeda N, Wakabayashi M, Nakamura K, Fukuda H, Nakamura S, Mitsudomi T, Watanabe SI, Asamura H, West Japan Oncology Group and Japan Clinical Oncology Group (2022) Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet 399(10335):1607–1617

Article  CAS  PubMed  Google Scholar 

Brunelli A, Decaluwe H, Gonzalez M, Gossot D, Petersen RH, Augustin F, Assouad J, Baste JM, Batirel H, Falcoz PE, Almanzar SF, Furak J, Gomez-Hernandez MT, de Antonio DG, Hansen H, Jimenez M, Koryllos A, Meacci E, Opitz I, Pages PB, Piwkowski C, Ruffini E, Schneiter D, Stupnik T, Szanto Z, Thomas P, Toker A, Tosi D, Veronesi G (2023) European society of thoracic surgeons expert consensus recommendations on technical standards of segmentectomy for primary lung cancer. Eur J Cardio-Thorac Surg. https://doi.org/10.1093/ejcts/ezad224

Article  Google Scholar 

Collins T, Pizarro D, Gasparini S, Bourdel N, Chauvet P, Canis M, Calvet L, Bartoli A (2021) Augmented reality guided laparoscopic surgery of the uterus. IEEE Trans Med Imaging 401:371–380

Article  Google Scholar 

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 148:1285–1294

Article  Google Scholar 

Schiavina R, Bianchi L, Lodi S, Cercenelli L, Chessa F, Bortolani B, Gaudiano C, Casablanca C, Droghetti M, Porreca A, Romagnoli D, Golfieri R, Giunchi F, Fiorentino M, Marcelli E, Diciotti S, Brunocilla E (2021) Real-time augmented reality three-dimensional guided robotic radical prostatectomy: preliminary experience and evaluation of the impact on surgical planning. Eur Urol Focus 76:1260–1267

Article  Google Scholar 

Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X (2020) Deep learning in medical image registration: a review. Phys Med Biol 6520:20TR01

Article  Google Scholar 

Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 2211:1330–1334. https://doi.org/10.1109/34.888718

Article  Google Scholar 

OpenCV. Camera Calibration and 3D Reconstruction. Open Source Computer Vision; [updated 17–10–2024; cited 2024 21–10–2024]; Available from: https://docs.opencv.org/4.10.0/d9/d0c/group__calib3d.html#ga617b1685d4059c6040827800e72ad2b6.

Sadeghi AH, Maat A, Taverne Y, Cornelissen R, Dingemans AC, Bogers A, Mahtab EAF (2021) Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies. JTCVS Tech 7:309–321

Article  PubMed  PubMed Central  Google Scholar 

van Rossum G, Drake FL.(2009) Python 3 reference manual. Scotts valley, CA: CreateSpace.

Bradski G (2000) The opencv library. Dr Dobb’s J 2511:120–125

Google Scholar 

Hirschmüller H, editor. Accurate and efficient stereo processing by semi-global matching and mutual information. Computer vision and pattern recognition; (2005).

Computer Vision Toolbox version (R2023b). (2023): The Mathworks Inc.; 2023; Available from: https://nl.mathworks.com/help/vision/.

Kikinis R, Pieper SD, Vosburgh KG. (2014). 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz FA, editor. Intraoperative imaging and image-guided therapy. New York, NY: Springer New York; p. 277–89.

Besl P, McKay HD (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256. https://doi.org/10.1109/34.121791

Article  Google Scholar 

Chen Y, Medioni G (1992) Object modeling by registration of multiple range images. Image Vision Comput 10:145–155. https://doi.org/10.1109/robot.1991.132043

Article  Google Scholar 

Doornbos M-CJ, Peek JJ, Maat APWM, Ruurda JP, De Backer P, Cornelissen BMW, Mahtab EAF, Sadeghi AH, Kluin J (2024) Augmented reality implementation in minimally invasive surgery for future application in pulmonary surgery: a systematic review. Surg Innov 316:646–658. https://doi.org/10.1177/15533506241290412

Article  Google Scholar 

Sadeghi AH, Mank Q, Tuzcu AS, Hofman J, Siregar S, Maat A, Mottrie A, Kluin J, De Backer P (2024) Artificial intelligence–assisted augmented reality robotic lung surgery: Navigating the future of thoracic surgery. JTCVS Tech 26:121–125. https://doi.org/10.1016/j.xjtc.2024.04.011

Article  PubMed  PubMed Central  Google Scholar 

Collins T, Bartoli A, Bourdel N, Canis M (2016) Robust, real-time, dense and deformable 3D organ tracking in laparoscopic videos. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention – MICCAI 2016: 19th international conference, Athens, Greece, October 17-21, 2016, Proceedings, Part I. Springer International Publishing, Cham, pp 404–412. https://doi.org/10.1007/978-3-319-46720-7_47

Chapter  Google Scholar 

Zhou L, Sun G, Li Y, Li W, Zhiyong S (2022) Point cloud denoising review: from classical to deep learning-based approaches. Gr Models 121:101140. https://doi.org/10.1016/j.gmod.2022.101140

Article  Google Scholar 

De Backer P, Van Praet C, Simoens J, Peraire Lores M, Creemers H, Mestdagh K, Allaeys C, Vermijs S, Piazza P, Mottaran A, Bravi CA, Paciotti M, Sarchi L, Farinha R, Puliatti S, Cisternino F, Ferraguti F, Debbaut C, De Naeyer G, Decaestecker K, Mottrie A (2023) Improving augmented reality through deep learning: real-time instrument delineation in robotic renal surgery. Eur Urol. https://doi.org/10.1016/j.eururo.2023.02.024

Article  PubMed  Google Scholar 

Penza V, Ortiz J, Mattos LS, Forgione A, De Momi E (2016) Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery. Int J Comput Assist Radiol Surg 112:197–206. https://doi.org/10.1007/s11548-015-1276-0

Article  Google Scholar 

Xi L, Zhao Y, Chen L, Gao QH, Tang W, Wan TR, Xue T (2021) Recovering dense 3D point clouds from single endoscopic image. Comput Methods Prog Biomed 205:106077. https://doi.org/10.1016/j.cmpb.2021.106077

Article  Google Scholar 

Pluim JPW, Muenzing SEA, Eppenhof KAJ, Murphy K, editors. (2016) The truth is hard to make: Validation of medical image registration. In 2016 23rd international conference on pattern recognition (ICPR); 4–8 Dec. 2016.

Chen S, Nan L, Xia R, Zhao J, Wonka P (2020) PLADE: a plane-based descriptor for point cloud registration with small overlap. IEEE Trans Geosci Remote Sens 584:2530–2540. https://doi.org/10.1109/TGRS.2019.2952086

Article  Google Scholar 

Malhotra S, Halabi O, Dakua SP, Padhan J, Paul S, Palliyali W (2023) Augmented reality in surgical navigation: a review of evaluation and validation metrics. Appl Sci 13(3):1629. https://doi.org/10.3390/app13031629

Article  CAS  Google Scholar 

Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohi P, Shotton J, Hodges S, Fitzgibbon A, editors. (2011) KinectFusion: real-time dense surface mapping and tracking. In 2011 10th IEEE international symposium on mixed and augmented reality; 26–29

Richa R, Bó APL, Poignet P (2011) Towards robust 3D visual tracking for motion compensation in beating heart surgery. Med Image Anal 15(3):302–315. https://doi.org/10.1016/j.media.2010.12.002

Article  PubMed  Google Scholar 

Ivashchenko O, Smit J, Ter Beek L, Rijkhorst E-J, Nijkamp J, Ruers T, Kuhlmann K (2021) Clinical implementation of in-house developed MR-based patient-specific 3D models of liver anatomy. Eur Surg Res. https://doi.org/10.1159/000513335

Article  Google Scholar