System events: readily accessible features for surgical phase detection
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Surgical phase recognition using sensor data is challenging due to high variation in patient anatomy and surgeon-specific operating styles. Segmenting surgical procedures into constituent phases is of significant utility for resident training, education, self-review, and context-aware operating room technologies. Phase annotation is a highly labor-intensive task and would benefit greatly from automated solutions.
We propose a novel approach using system events—for example, activation of cautery tools—that are easily captured in most surgical procedures. Our method involves extracting event-based features over 90-s intervals and assigning a phase label to each interval. We explore three classification techniques: support vector machines, random forests, and temporal convolution neural networks. Each of these models independently predicts a label for each time interval. We also examine segmental inference using an approach based on the semi-Markov conditional random field, which jointly performs phase segmentation and classification. Our method is evaluated on a data set of 24 robot-assisted hysterectomy procedures.
Our framework is able to detect surgical phases with an accuracy of 74 % using event-based features over a set of five different phases—ligation, dissection, colpotomy, cuff closure, and background. Precision and recall values for the cuff closure (Precision: 83 %, Recall: 98 %) and dissection (Precision: 75 %, Recall: 88 %) classes were higher than other classes. The normalized Levenshtein distance between predicted and ground truth phase sequence was 25 %.
Our findings demonstrate that system events features are useful for automatically detecting surgical phase. Events contain phase information that cannot be obtained from motion data and that would require advanced computer vision algorithms to extract from a video. Many of these events are not specific to robotic surgery and can easily be recorded in non-robotic surgical modalities. In future work, we plan to combine information from system events, tool motion, and videos to automate phase detection in surgical procedures.
KeywordsSurgical phase detection System events Sensor data Surgical workflow analysis Robot-assisted surgery Surgical task flow Surgical process modeling
- 3.Ahmadi SA, Sielhorst T, Stauder R, Horn M, Feussner H, Navab N (2006) Recovery of surgical workflow without explicit models. In: Larsen R, Nielsen M, Sporring J (eds) Medical image computing and computer-assisted intervention—MICCAI 2006. Lecture notes in computer science, vol 4190. Springer, Berlin, Heidelberg, pp 420–428Google Scholar
- 4.Padoy N, Blum T, Essa I, Feussner H, Berger MO, Navab N (2007) A boosted segmentation method for surgical workflow analysis. In: Ayache N, Ourselin S, Maeder A (eds) Medical image computing and computer-assisted intervention—MICCAI 2007. Lecture notes in computer science, vol 4791. Springer, Berlin, Heidelberg, pp 102–109Google Scholar
- 5.Blum T, Feussner H, Navab N (2010) Modeling and segmentation of surgical workflow from laparoscopic video. In: Jiang T, Navab N, Pluim JPW, Viergever MA (eds) Medical image computing and computer-assisted intervention—MICCAI 2010. Lecture notes in computer science, vol 6363. Springer, Berlin, Heidelberg, pp 400–407Google Scholar
- 7.Stauder R, Okur A, Peter L, Schneider A, Kranzfelder M, Feussner H, Navab N (2014) Random forests for phase detection in surgical workflow analysis. In: Stoyanov D, Collins DL, Sakuma I, Abolmaesumi P, Jannin P (eds) Information processing in computer-assisted interventions, no. 8498 in lecture notes in computer science, Springer International Publishing, pp 148–157Google Scholar
- 8.DiPietro R, Stauder R, Kayis E, Schneider A, Kranzfelder M, Feussner H, Hager GD, Navab N (2015) Automated surgical-phase recognition using rapidly-deployable sensors. In: Modeling and monitoring of computer assisted interventions (M2CAI)Google Scholar
- 9.Neumuth T, Straub G, Meixensberger J, Lemke HU, Burgert O (2006) Acquisition of process descriptions from surgical interventions. In: Bressan S, Kung J, Wagner R (eds) Database and expert systems applications, no. 4080 in lecture notes in computer science, Springer, Berlin, pp 602–611. doi:10.1007/11827405_59
- 11.Katic D, Wekerle AL, Gartner F, Kenngott H, Muller-Stich BP, Dillmann R, Speidel S (2014) Knowledge-driven formalization of laparoscopic surgeries for rule-based intraoperative context-aware assistance. In: Stoyanov D, Collins DL, Sakuma I, Abolmaesumi P, Jannin P (eds) Information processing in computer-assisted intervention. Lecture notes in computer science, vol 8498. Springer, Switzerland, pp 158–167Google Scholar
- 12.Twinanda AP, Marescaux J, Mathelin Md, Padoy N (2015) Classification approach for automatic laparoscopic video database organization. Int J Comput Assist Radiol Surg. doi:10.1007/s11548-015-1183-4
- 15.Varadarajan B (2011) Learning and inference algorithms for dynamical system models of dextrous motion, Dissertation, The Johns Hopkins UniversityGoogle Scholar
- 18.Tao L, Zappella L, Hager GD, Vidal R (2013) Surgical gesture segmentation and recognition. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention—MICCAI 2013. Lecture notes in computer science, vol 8151. Springer, Berlin, pp 339–346Google Scholar
- 19.Lea C, Vidal R, Hager GD (2016) Learning convolutional action primitives for fine-grained action recognition. IEEE international conference on robotics and automation, Stockholm (accepted)Google Scholar
- 21.Lea C, Reiter A, Vidal R, Hager GD (2016) Efficient segmental inference for spatiotemporal modeling of fine-grained actions. arXiv:1602.02995 [cs]
- 22.Sarawagi S, Cohen WW (2005) Semi-Markov conditional random fields for information extraction. In: Advances in neural information processing systems 17. MIT Press, Cambridge, pp 1185–1192. http://papers.nips.cc/paper/2648-semi-markov-conditional-random-fields-for-information-extraction.pdf
- 23.Chen CCG, Tanner E, Malpani A, Vedula SS, Fader A, Scheib S, Hager GD (2015) Warm-up before robotic hysterectomy does not improve trainee operative performance: a randomized trial. In: American urogynecologic society annual meeting, pp 396–401Google Scholar
- 24.DiMaio SP, Hasser C (2008), The da Vinci research interface, http://www.midasjournal.org/browse/publication/622