Abstract
Purpose
Automatic online recognition of surgical instruments is required to monitor instrument use for surgical process modeling. A system was developed and tested using available technologies.
Methods
A recognition system was developed using RFID technology to identify surgical activities. Information fusion for online recognition of surgical process models was conceived as a layer model to abstract information from specific sensor technologies. Redundant, complementary, and cooperative sensor signal fusion was used in the layer model to increase the surgical instrument recognition rate. Several different information fusion strategies were evaluated for situation recognition abilities in a mock-up environment based on simulations of surgical processes.
Results
This information fusion system was able to reliably detect, identify, and localize surgical instruments in an interventional suite. A combination of information fusion strategies was able to achieve a correct classification rate of 97% and was as effective as observer-based acquisition methods.
Conclusion
Different information fusion strategies for the recognition of surgical instruments were evaluated, showing that redundant, complementary, and cooperative information fusion is feasible for recognition of surgical work steps. A combination of sensor- and observer-based modeling strategies provides the most robust solution for surgical process models.
Similar content being viewed by others
References
Cleary K, Kinsella A, Mun SK (2005) OR2020 workshop report: operating room of the future. In: Lemke HU, Inamura K, Doi K, Vannier MW, Farmann AG (eds) CARS 2005 computer assisted radiology and surgery. Elsevier, Amsterdam, pp 832–838
Lemke HU, Vannier MW (2006) The operating room and the need for an IT infrastructure and standards. Int J Comput Assist Radiol Surg 1: 117–121
Neumuth T, Jannin P, Schlomberg J, Meixensberger J, Wiedemann P, Burgert O (2011) Analysis of surgical intervention populations using generic surgical process models. Int J Comput Assist Radiol Surg 6: 59–71
Blum T, Padoy N, Feußner H, Navab N (2008) Workflow mining for visualization and analysis of surgeries. Int J Comput Assist Radiol Surg 3: 379–386
Padoy N, Blum T, Ahmadi SA, Feussner H, Berger MO, Navab N (2010) Statistical modeling and recognition of surgical workflow. Med Image Anal. doi:10.1016/j.media.2010.10.001
Sudra G, Becker A, Braun M, Speidel S, Mueller-Stich BP, Dillmann R (2009) Estimating similarity of surgical situations with case-retrieval-nets. Stud Health Technol Inform 142: 358–363
Lalys F, Riffaud L, Morandi X, Jannin P (2010) Automatic phases recognition in pituitary surgeries by microscope images classification. Presented at IPCAI 2010
Bouarfa L, Jonker PP, Dankelman J (2011) Discovery of high-level tasks in the operating room. J Biomed Inform 44: 455–462
Lin H, Hager G (2009) User-independent models of manipulation using video contextual cues. Presented at M2CAI-Workshop 2009
Varadarajan B, Reiley C, Lin H, Khudanpur S, Hager G (2009) Data-derived models for segmentation with application to surgical assessment and training. Med Image Comput Comput Assist Interv 12: 426–434
Darzi A, Mackay S (2002) Skills assessment of surgeons. Surgery 131: 121–124
Rosen J, Hannaford B, Richards CG, Sinanan MN (2001) Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans Biomed Eng 48: 579–591
Ahmadi A, Padoy N, Rybachuk K, Feußner H, Heining S, Navab N (2009) Motif discovery in OR sensor data with application to surgical workflow analysis and activity detection. Presented at M2CAI-Workshop 2009
Xiao Y, Hu P, Hu H, Ho D, Dexter F, Mackenzie CF, Seagull FJ, Dutton RP (2005) An algorithm for processing vital sign monitoring data to remotely identify operating room occupancy in real-time. Anesth Analg 101: 823–829
Lin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11: 220–230
James A, Vieira D, Lo B, Darzi A, Yang GZ (2007) Eye-gaze driven surgical workflow segmentation. Med Image Comput Comput Assist Interv 10: 110–117
Nara A, Izumi K, Iseki H, Suzuki T, Nambu K, Sakurai Y (2010) Surgical workflow analysis based on staff’s trajectory patterns. Presented at M2CAI-Workshop 2009
Bhatia B, Oates T, Xiao Y, Hu P (2007) Real-time identification of operating room state from video. In: Holte C, Howe A (eds) Proceedings of the 21st AAAI conference on artificial intelligence. AAAI Organization. pp 1761–1766
MacKenzie CL, Ibbotson JA, Cao CGL, Lomax AJ (2001) Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment. Min Invas Ther Allied Technol 10: 121–127
Jannin P, Raimbault M, Morandi X, Riffaud L, Gibaud B (2003) Model of surgical procedures for multimodal image-guided neurosurgery. Comput Aided Surg 8: 98–106
Jannin P, Morandi X (2007) Surgical models for computer-assisted neurosurgery. NeuroImage 37: 783–791
Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O (2009) Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc 16: 72–80
Neumuth T, Trantakis C, Riffaud L, Strauss G, Meixensberger J, Burgert O (2009) Assessment of technical needs for surgical equipment by surgical process models. Min Invas Ther Allied Technol 18: 841–849
Neumuth T, Krauss A, Meixensberger J, Muensterer O (2011) Impact quantification of the DaVinci Telemanipulator system on the surgical workflow using resource impact profiles. Int J Med Robot 7: 156–164
Hall D, LLinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85: 6–23
Xiong N, Svensson P (2002) Multi-sensor management for information fusion: issues and approaches. Inf. Fusion 3: 163–186
Llinas J, Bowman C, Rogova G, Steinberg A, Waltz E, White F (2004) Revisiting the JDL data fusion model II. In: Svensson P, Schubert J (eds) Proceedings of the 7th international conference on information fusion. pp 1218–1230
Kokar MM, Tomasik JA, Weyman J (2004) Formalizing classes of information fusion systems. Inf Fusion 5: 189–202
Durrant-Whyte HF (1988) Sensor models and multisensor integration. Int J Robot Res 7: 97–113
Luo RC, Yih CC, Su KL (2002) Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sens J 2: 107–119
MathWorks, MATLAB. Natick, MA, 2009
SPSS Inc., SPSS 15.0. Chicago, 2009
van der Togt R, van Lieshout ER, Hensbroek R, Beinat E, Binnekade JM, Bakker PJM (2008) Electromagnetic interference from radio frequency identification inducing potentially hazardous incidents in critical care medical equipment. J Am Med Assoc 299: 2884–2890
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Neumuth, T., Meißner, C. Online recognition of surgical instruments by information fusion. Int J CARS 7, 297–304 (2012). https://doi.org/10.1007/s11548-011-0662-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-011-0662-5