Abstract
Appropriate visualization of endoscopic surgery recordings has a huge potential to benefit surgical work life. For example, it enables surgeons to quickly browse medical interventions for purposes of documentation, medical research, discussion with colleagues, and training of young surgeons. Current literature on automatic action recognition for endoscopic surgery covers domains where surgeries follow a standardized pattern, such as cholecystectomy. However, there is a lack of support in domains where such standardization is not possible, such as gynecologic laparoscopy. We provide ActionVis, an interactive tool enabling surgeons to quickly browse endoscopic recordings. Our tool analyses the results of a post-processing of the recorded surgery. Information on individual frames are aggregated temporally into a set of scenes representing frequent surgical actions in gynecologic laparoscopy, which help surgeons to navigate within endoscopic recordings in this domain.
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References
Hajj, H.A., Lamard, M., Charrière, K., Cochener, B., Quellec, G.: Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2002–2005, July 2017
Loukas, C., Nikiteas, N., Schizas, D., Georgiou, E.: Shot boundary detection in endoscopic surgery videos using a variational bayesian framework. Int. J. Comput. Assist. Radiol. Surg. 11(11), 1937–1949 (2016)
McCrory, B., LaGrange, C.A., Hallbeck, M.: Quality and safety of minimally invasive surgery: past, present, and future. Biomed. Eng. Comput. Biol. 6, 1 (2014)
Petscharnig, S., Schöffmann, K.: Learning laparoscopic video shot classication for gynecological surgery. Multimedia Tools Appl. 1–19 (2017). https://doi.org/10.1007/s11042-017-4699-5
Quellec, G., Lamard, M., Cochener, B., Cazuguel, G.: Real-time segmentation and recognition of surgical tasks in cataract surgery videos. IEEE Trans. Med. Imaging 33(12), 2352–2360 (2014)
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N.: Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86–97 (2017)
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Petscharnig, S., Schoeffmann, K. (2018). ActionVis: An Explorative Tool to Visualize Surgical Actions in Gynecologic Laparoscopy. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_30
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DOI: https://doi.org/10.1007/978-3-319-73600-6_30
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