Towards real-time multiple surgical tool tracking

References

• Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 34:2274–2282.
   Google Scholar
• Allan M, Shvets A, Kurmann T, Zhang Z, Duggal R, Su YH, Rieke N, Laina I. 2017. 2017 robotic instrument segmentation challenge; p. 1–14.
   Google Scholar
• Bergmann P, Meinhardt T, Leal-Taixe L. 2019. Tracking without bells and whistles.
   Google Scholar
• Bernardin K, Stiefelhagen R. Evaluating multiple object tracking performance: The CLEAR MOT metrics. Eurasip J Image Video Process 2008;2008(1):1–10.
   Google Scholar
• Bewley A, Ge Z, Ott L, Ramos F, Upcroft B. 2016. Simple online and realtime tracking. Proceedings - International Conference on Image Processing, ICIP 2016-August; Phoenix, AZ. p. 3464–3468.
   Google Scholar
• Bodenstedt S, Allan M, Agustinos A, Du X, Garcia-Peraza-Herrera L, Kenngott H, Kurmann T, Müller-Stich B, Ourselin S, Pakhomov D, et al. 2018. Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery.
   Google Scholar
• Bouget D, Allan M, Stoyanov D, Jannin P. 2017. Vision-based and marker-less surgical tool detection and tracking: a review of the literature. Med Image Anal. 35:633–654.
   Google Scholar
• Colleoni E, Moccia S, Du X, De Momi E, Stoyanov D. 2019. Deep learning based robotic tool detection and articulation estimation with spatio-temporal layers. IEEE Rob Autom Lett. 4:2714–2721. doi:10.1109/LRA.2019.2917163.
   Google Scholar
• Du X, Allan M, Bodenstedt S, Maier-hein L, Speidel S, Dore A, Stoyanov D. 2019. Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking in surgical video. Med Image Anal. 57:120–135.
   Google Scholar
• Du X, Kurmann T, Chang PL, Allan M, Ourselin S, Sznitman R, Kelly JD, Stoyanov D. 2018. Articulated multi-instrument 2-d pose estimation using fully convolutional networks. IEEE Trans Med Imaging. 37:1276–1287. doi:10.1109/TMI.2017.2787672.
   Google Scholar
• Grammatikopoulou M, Flouty E, Kadkhodamohammadi A, Quellec G, Chow A, Nehme J, Luengo I, Stoyanov D, CaDIS: Cataract dataset for image segmentation. 2020. p. 1–8 [accessed 2020 May]. http://arxiv.org/abs/1906.11586.
   Google Scholar
• Jin Y, Cheng K, Dou Q, Heng PA. 2019. Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video, in medical image computing and computer assisted intervention – MICCAI 2019; p. 440–448.
   Google Scholar
• Lloyd S. 1982. Least squares quantization in PCM. IEEE Trans Inf Theory. 28:129–137. doi:10.1109/TIT.1982.1056489.
   Google Scholar
• Milan A, Leal-Taixe L, Reid I, Roth S, Schindler K. 2016. MOT16: Benchmark Multi-Object Tracking; p. 1–12.
   Google Scholar
• Munkres J. 1957. Algorithms for the assignment and transportation problems. J Soc Ind Appl Math. 5:32–38.
   Google Scholar
• Nwoye CI, Mutter D, Marescaux J, Padoy N. 2019. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. Int J Comput Assist Radiol Surg. 14:1059–1067.
   Google Scholar
• Ross T, Reinke A, Full PM, Wagner M, Kenngott H, Apitz M, Hempe H, Filimon DM, Scholz P, Tran TN, et al. 2020. Robust medical instrument segmentation challenge 2019.
   Google Scholar
• Ryu J, Choi J, Kim HC. 2013. Endoscopic vision-based tracking of multiple surgical instruments during robot-assisted surgery. Artif Organs. 37:107–112.
   Google Scholar
• Wojke N, Bewley A, Paulus D. 2018. Simple online and realtime tracking with a deep association metric Proceedings - International Conference on Image Processing, ICIP 2017-September; Beijing, China. p. 3645–3649.
   Google Scholar
• Yang F, Lu H, Yang MH. 2014. Robust superpixel tracking. IEEE Trans Image Process. 23:1639–1651.
   Google Scholar
• Ye M, Zhang L, Giannarou S, Yang GZ. 2016. Real-time 3D tracking of articulated tools for robotic surgery. Lect Notes Comput Sci. 9900:386–394. Springer International Publishing, Cham.
   Google Scholar