Abstract
Over the past one hundred years, the classic teaching methodology of “see one, do one, teach one” has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.
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References
Criss, K., McNames, J.: Video assessment of finger tapping for Parkinson’s disease and other movement disorders. In: IEEE International Conference on Engineering in Medicine and Biology Society, pp. 7123–7126 (2011)
Evangelidis, G.D., Bauckhage, C.: Efficient and robust alignment of unsynchronized video sequences. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 286–295. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23123-0_29
Forestier, G., Petitjean, F., Riffaud, L., Jannin, P.: Non-linear temporal scaling of surgical processes. Artif. Intell. Med. 62(3), 143–152 (2014)
Forestier, G., et al.: Surgical motion analysis using discriminative interpretable patterns. Artif. Intell. Med. 91, 3–11 (2018)
Gao, Y., et al.: The JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS): a surgical activity dataset for human motion modeling. In: Modeling and Monitoring of Computer Assisted Interventions - MICCAI Workshop (2014)
Herrera-Almario, G.E., Kirk, K., Guerrero, V.T., Jeong, K., Kim, S., Hamad, G.G.: The effect of video review of resident laparoscopic surgical skills measured by self- and external assessment. Am. J. Surg. 211(2), 315–320 (2016)
Intuitive Surgical Sunnyvale, C.A.: The Da Vinci surgical system. https://www.intuitive.com/en/products-and-services/da-vinci
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Evaluating surgical skills from kinematic data using convolutional neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 214–221. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_25
Kneebone, R., Kidd, J., Nestel, D., Asvall, S., Paraskeva, P., Darzi, A.: An innovative model for teaching and learning clinical procedures. Med. Educ. 36(7), 628–634 (2002)
Li, Z., Huang, Y., Cai, M., Sato, Y.: Manipulation-skill assessment from videos with spatial attention network. ArXiv (2019)
Masic, I.: E-learning as new method of medical education. Acta informatica medica 16(2), 102 (2008)
McNatt, S., Smith, C.: A computer-based laparoscopic skills assessment device differentiates experienced from novice laparoscopic surgeons. Surg. Endosc. 15(10), 1085–1089 (2001)
Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K.: Evaluation of evidence-based practices in online learning: a meta-analysis and review of online learning studies (2009)
Mota, P., Carvalho, N., Carvalho-Dias, E., Costa, M.J., Correia-Pinto, J., Lima, E.: Video-based surgical learning: improving trainee education and preparation for surgery. J. Surg. Educ. 75(3), 828–835 (2018)
Padua, F., Carceroni, R., Santos, G., Kutulakos, K.: Linear sequence-to-sequence alignment. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 304–320 (2010)
Petitjean, F., Forestier, G., Webb, G.I., Nicholson, A.E., Chen, Y., Keogh, E.: Dynamic time warping averaging of time series allows faster and more accurate classification. In: IEEE International Conference on Data Mining, pp. 470–479 (2014)
Petitjean, F., Gançarski, P.: Summarizing a set of time series by averaging: from Steiner sequence to compact multiple alignment. Theoret. Comput. Sci. 414(1), 76–91 (2012)
Petitjean, F., Ketterlin, A., Gançarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognit. 44(3), 678–693 (2011)
Rapp, A.K., Healy, M.G., Charlton, M.E., Keith, J.N., Rosenbaum, M.E., Kapadia, M.R.: Youtube is the most frequently used educational video source for surgical preparation. J. Surg. Educ. 73(6), 1072–1076 (2016)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)
Shokoohi-Yekta, M., Hu, B., Jin, H., Wang, J., Keogh, E.: Generalizing dtw to the multi-dimensional case requires an adaptive approach. Data Min. Knowl. Disc. 31(1), 1–31 (2017)
Smith, T.L., Ransbottom, S.: Digital video in education. In: Distance Learning Technologies: Issues, Trends and Opportunities, pp. 124–142 (2000)
Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1(4), 337–348 (1994)
Wang, O., Schroers, C., Zimmer, H., Gross, M., Sorkine-Hornung, A.: Videosnapping: interactive synchronization of multiple videos. ACM Trans. Graph. 33(4), 77 (2014)
Wedge, D., Kovesi, P., Huynh, D.: Trajectory based video sequence synchronization. In: Digital Image Computing: Techniques and Applications, p. 13 (2005)
Wolf, L., Zomet, A.: Sequence-to-sequence self calibration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 370–382. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47967-8_25
Yamada, Y., Kobayashi, M.: Detecting mental fatigue from eye-tracking data gathered while watching video. In: ten Teije, A., Popow, C., Holmes, J.H., Sacchi, L. (eds.) AIME 2017. LNCS (LNAI), vol. 10259, pp. 295–304. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59758-4_34
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Ismail Fawaz, H., Forestier, G., Weber, J., Petitjean, F., Idoumghar, L., Muller, PA. (2019). Automatic Alignment of Surgical Videos Using Kinematic Data. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_14
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