Abstract
The augmented reality (AR) is the latest technology in laparoscopy and minimally invasive surgery (MIS). This technology decreases post-operative pain, recovery time, difficulty rate, and infections. The main limitations of AR systems are system accuracy, the depth perception of organs, and real-time laparoscopy view. The aim of this work is to define the required components to implement an efficient AR visualization system. This work introduces Data, Visualization techniques, and View (DVV) classification system. The components of DVV should be considered and used as validation criteria for introducing any AR visualization system into the operating room. Well-designed DVV system can help the end user and surgeons with a clear view of anatomical details during abdominal surgery. This study validates the DVV taxonomy and considers system comparison, completeness, and acceptance as the primary criteria upon which the proposed DVV classification is based upon. This work also introduces a framework in which AR systems can be discussed, analyzed, validated and evaluated. State_of_the_art solutions are classified, evaluated, validated, and verified to describe how they worked in the domain of AR visualizing laparoscopy during abdominal surgery in the operating room. Finally, this paper states how the proposed system improves the system limitations.
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References
Barcali, E., Iadanza, E., Manetti, L., Francia, P., Nardi, C., Bocchi, L.: Augmented reality in surgery: a scoping review. Appl. Sci. 12(14), 6890 (2022)
Bernhardt, S., Nicolau, S.A., Soler, L., Doignon, C.: The status of augmented reality in laparoscopic surgery as of 2016. Med. Image Anal. 37, 66–90 (2017)
Bourdel, N., et al.: Use of augmented reality in laparoscopic gynecology to visualize myomas. Fertility Sterility 107(3), 737–739 (2017)
Chen, L., Tang, W., John, N.W., Wan, T.R., Zhang, J.J.: Slam-based dense surface reconstruction in monocular minimally invasive surgery and its application to augmented reality. Comput. Methods Programs Biomed. 158, 135–146 (2018)
Cheung, T.T., et al.: Pure laparoscopic hepatectomy with augmented reality-assisted indocyanine green fluorescence versus open hepatectomy for hepatocellular carcinoma with liver cirrhosis: a propensity analysis at a single center. Asian J. Endoscopic Surge 11(2), 104–111 (2018)
Coelho, G., et al.: The potential applications of augmented reality in fetoscopic surgery for antenatal treatment of myelomeningocele. World Neurosurg. 159, 27–32 (2022)
Collins, T., et al.: Augmented reality guided laparoscopic surgery of the uterus. IEEE Trans. Med. Imaging 40(1), 371–380 (2021)
Fucentese, S.F., Koch, P.P.: A novel augmented reality-based surgical guidance system for total knee arthroplasty. Arch. Orthop. Trauma Surg. 141(12), 2227–2233 (2021). https://doi.org/10.1007/s00402-021-04204-4
Ganni, S., Botden, S.M.B.I., Chmarra, M., Goossens, R.H.M., Jakimowicz, J.J.: A software-based tool for video motion tracking in the surgical skills assessment landscape. Surg. Endosc. 32(6), 2994–2999 (2018). https://doi.org/10.1007/s00464-018-6023-5
Golse, N., Petit, A., Lewin, M., Vibert, E., Cotin, S.: Augmented reality during open liver surgery using a markerless non-rigid registration system. J. Gastrointest. Surg. 25(3), 662–671 (2021)
Ivanov, V.M., et al.: Practical application of augmented/mixed reality technologies in surgery of abdominal cancer patients. J. Imaging 8(7), 183 (2022)
Kenngott, H.G., et al.: Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study. Surg. Endosc. 32(6), 2958–2967 (2018). https://doi.org/10.1007/s00464-018-6151-y
Kersten-Oertel, M., Jannin, P., Collins, D.L.: DVV: a taxonomy for mixed reality visualization in image guided surgery. IEEE Trans. Visual Comput. Graphics 18(2), 332–352 (2011)
Rowe, S.P., Johnson, P.T., Fishman, E.K.: Cinematic rendering of cardiac CT volumetric data: principles and initial observations. J. Cardiovasc. Comput. Tomogr. 12(1), 56–59 (2018)
Zhao, S., Zhang, W., Sheng, W., Zhao, X.: A frame of 3d printing data generation method extracted from CT data. Sens. Imaging 19(1), 1–13 (2018)
Zhao, Z., et al.: Augmented reality technology in image-guided therapy: state-of-the-art review. Proc. Inst. Mech. Eng. Part H. J. Eng. Med. 235(12), 1386–1398 (2021)
Zou, Y., Liu, P.X.: A high-resolution model for soft tissue deformation based on point primitives. Comput. Methods Programs Biomed. 148, 113–121 (2017)
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Bikram, K.R., Al-Dala’in, T., Alkhawaldeh, R.S., AlSallami, N., Al-Jerew, O., Ahmed, S. (2023). Taxonomy of AR to Visualize Laparoscopy During Abdominal Surgery. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_25
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DOI: https://doi.org/10.1007/978-3-031-35308-6_25
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