Automatic phase prediction from low-level surgical activities
Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms.
In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm.
Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise.
We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions.
KeywordsSurgical process Temporal analysis Prediction Surgery
The authors would like to thanks all the surgeons of the Neurosurgery Department of the Leipzig University Hospital, Germany involved in this work. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the manuscript.
- 1.Bardram JE, Doryab A, Jensen RM, Lange PM, Nielsen KL, Petersen ST (2011) Phase recognition during surgical procedures using embedded and body-worn sensors. In: IEEE international conference on pervasive computing and communications, pp 45–53Google Scholar
- 5.Forestier G, Lalys F, Riffaud L, Collins DL, Meixensberger J, Wassef SN, Neumuth T, Goulet B, Jannin P (2013) Multi-site study of surgical practice in neurosurgery based on surgical process models. J Biomed Inform 46(5):822–829Google Scholar
- 6.Forestier G, Lalys F, Riffaud L, Trelhu B, Jannin P (2012) Classification of surgical processes using dynamic time warping. J Biomed Inform 45(2):255–264Google Scholar
- 11.Lalys F, Jannin P (2013) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 8(5):1–17Google Scholar
- 12.Lalys F, Riffaud L, Morandi X, Jannin P (2010) Automatic phases recognition in pituitary surgeries by microscope images classification. In: Information processing in computer-assisted interventions, vol 6135. Springer, pp 34–44Google Scholar
- 14.Meißner C, Meixensberger J, Pretschner A, Neumuth T (2014) Sensor-based surgical activity recognition in unconstrained environments. Minimally Invasive Therapy & Allied TechnologiesGoogle Scholar
- 15.Padoy N, Blum T, Feussner H, Berger MO, Navab N (2008) On-line recognition of surgical activity for monitoring in the operating room. In: AAAI, pp 1718–1724Google Scholar
- 16.Padoy N, Mateus D, Weinland D, Berger MO, Navab N (2009) Workflow monitoring based on 3d motion features. In: IEEE international conference on computer vision workshops, pp 585–592 (2009)Google Scholar
- 17.Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San MateoGoogle Scholar
- 19.Sebban M, NockO R, Chauchat J, Rakotomalala R (2000) Impact of learning set quality and size on decision tree performances. IJCSS 1(1):85Google Scholar
- 20.Shi Y, Bobick A, Essa I (2006) Learning temporal sequence model from partially labeled data. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE, pp 1631–1638Google Scholar
- 21.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: Information processing in computer-assisted interventions. Springer, pp 148–157Google Scholar
- 22.Varadarajan B, Reiley C, Lin H, Khudanpur S, Hager G (2009) Data-derived models for segmentation with application to surgical assessment and training. In: Medical image computing and computer-assisted intervention-MICCAI 2009. Springer, pp 426–434Google Scholar
- 23.Ženko B (2008) Learning predictive clustering rules. Informatica 32:95–96Google Scholar