Page 51 - Read Online
P. 51

Ding et al. Art Int Surg 2024;4:109-38  https://dx.doi.org/10.20517/ais.2024.16    Page 135

               191.     Torralba A, Oliva A. Depth estimation from image structure. IEEE Trans Pattern Anal Mach Intell 2002;24:1226-38.  DOI
               192.     Marr D, Poggio T. Cooperative computation of stereo disparity: a cooperative algorithm is derived for extracting disparity
                    information from stereo image pairs. Science 1976;194:283-7.  DOI  PubMed
               193.     Szeliski R. Computer vision: algorithms and applications. Springer. 2022.  DOI
               194.      Hannah MJ. Computer matching of areas in stereo images. Stanford University. 1974. Available from: https://www.semanticscholar.
                    org/paper/Computer-matching-of-areas-in-stereo-images.-Hannah/02a0829a658e7dbfdf49e8112b38f8911a12eb76. [Last accessed on
                    3 Jul 2024].
               195.      Stoyanov D, Darzi A, Yang GZ. A practical approach towards accurate dense 3D depth recovery for robotic laparoscopic surgery.
                    Comput Aided Surg 2005;10:199-208.  DOI  PubMed
               196.      Arnold RD. Automated stereo perception. PhD thesis, Stanford University (1983). Available from: https://searchworks.stanford.edu/
                    view/1052936. [Last accessed on 3 Jul 2024]
               197.      Okutomi M, Kanade T. A locally adaptive window for signal matching. Int J Comput Vision 1992;7:143-62.  DOI
               198.      Szeliski R, Coughlan J. Spline-based image registration. Int J Comput Vis 1997;22:199-218.  DOI
               199.      Lo B, Scarzanella MV, Stoyanov D, Yang G. Belief propagation for depth cue fusion in minimally invasive surgery. In: Metaxas D,
                    Axel L, Fichtinger G, Székely G, editors. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008. Berlin:
                    Springer; 2008. pp. 104-12.  DOI
               200.      Sinha RY, Raje SR, Rao GA. Three-dimensional laparoscopy: principles and practice. J Minim Access Surg 2017;13:165-9.  DOI
                    PubMed  PMC
               201.      Mueller-Richter UD, Limberger A, Weber P, Ruprecht KW, Spitzer W, Schilling M. Possibilities and limitations of current stereo-
                    endoscopy. Surg Endosc 2004;18:942-7.  DOI  PubMed
               202.      Bogdanova R, Boulanger P, Zheng B. Depth perception of surgeons in minimally invasive surgery. Surg Innov 2016;23:515-24.  DOI
                    PubMed
               203.      Sinha R, Sundaram M, Raje S, Rao G, Sinha M, Sinha R. 3D laparoscopy: technique and initial experience in 451 cases. Gynecol
                    Surg 2013;10:123-8.  DOI
               204.      Liu X, Sinha A, Ishii M, et al. Dense depth estimation in monocular endoscopy with self-supervised learning methods. IEEE Trans
                    Med Imaging 2020;39:1438-47.  DOI  PubMed  PMC
               205.      Li L, Li X, Yang S, Ding S, Jolfaei A, Zheng X. Unsupervised-learning-based continuous depth and motion estimation with
                    monocular endoscopy for virtual reality minimally invasive surgery. IEEE Trans Ind Inf 2021;17:3920-8.  DOI
               206.      Liu F, Shen C, Lin G. Deep convolutional neural fields for depth estimation from a single image. In: 2015 IEEE Conference on
                    Computer Vision and Pattern Recognition (CVPR); 2015 Jun 7-12; Boston, MA, USA. IEEE; 2014. pp. 5162-70.  DOI
               207.      Visentini-Scarzanella M, Sugiura T, Kaneko T, Koto S. Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.
                    Int J Comput Assist Radiol Surg 2017;12:1089-99.  DOI  PubMed
               208.      Oda M, Itoh H, Tanaka K, et al. Depth estimation from single-shot monocular endoscope image using image domain adaptation and
                    edge-aware depth estimation. Comput Methods Biomech Biomed Eng Imaging Vis 2022;10:266-73.  DOI
               209.      Zhan H, Garg R, Weerasekera CS, Li K, Agarwal H, Reid I. Unsupervised learning of monocular depth estimation and visual
                    odometry with deep feature reconstruction. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018 Jun
                    18-23; Salt Lake City, UT, USA. IEEE; 2018. pp. 340-9.  DOI
               210.      Liu F, Jonmohamadi Y, Maicas G, Pandey AK, Carneiro G. Self-supervised depth estimation to regularise semantic segmentation in
                    knee arthroscopy. In: Martel AL, et al., editors. Medical Image Computing and Computer Assisted Intervention - MICCAI 2020.
                    Cham: Springer; 2020. pp. 594-603.  DOI
               211.      Mahmood F, Chen R, Durr NJ. Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE
                    Trans Med Imaging 2018;37:2572-81.  DOI  PubMed
               212.      Guo R, Ayinde B, Sun H, Muralidharan H, Oguchi K. Monocular depth estimation using synthetic images with shadow removal. In:
                    2019 IEEE Intelligent Transportation Systems Conference (ITSC); 2019 Oct 27-30; Auckland, New Zealand. IEEE; 2019. pp. 1432-
                    9.  DOI
               213.      Chen RJ, Bobrow TL, Athe T, Mahmood F, Durr NJ. SLAM endoscopy enhanced by adversarial depth prediction. arXiv. [Preprint.]
                    Jun 29, 2019 [accessed 2024 Jul 3]. Available from: https://arxiv.org/abs/1907.00283.
               214.      Schreiber AM, Hong M, Rozenblit JW. Monocular depth estimation using synthetic data for an augmented reality training system in
                    laparoscopic surgery. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2021 Oct 17-20;
                    Melbourne, Australia. IEEE; 2021. pp. 2121-6.  DOI
               215.      Tong HS, Ng YL, Liu Z, et al. Real-to-virtual domain transfer-based depth estimation for real-time 3D annotation in transnasal
                    surgery: a study of annotation accuracy and stability. Int J Comput Assist Radiol Surg 2021;16:731-9.  DOI  PubMed  PMC
               216.      Wong A, Soatto S. Bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction. In: 2019
                    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long Beach, CA, USA. IEEE; 2019.
                    pp. 5637-46.  DOI
               217.      Widya AR, Monno Y, Okutomi M, Suzuki S, Gotoda T, Miki K. Learning-based depth and pose estimation for monocular endoscope
                    with loss generalization. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:3547-52.  DOI  PubMed
               218.      Shao S, Pei Z, Chen W, et al. Self-Supervised monocular depth and ego-Motion estimation in endoscopy: appearance flow to the
                    rescue. Med Image Anal 2022;77:102338.  DOI
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