JRM Vol.24 No.5 pp. 791-801
doi: 10.20965/jrm.2012.p0791


Automatic Surgical Workflow Estimation Method for Brain Tumor Resection Using Surgical Navigation Information

Ryoichi Nakamura*, Tomoaki Aizawa*, Yoshihiro Muragaki**,***,
Takashi Maruyama**,***, and Hiroshi Iseki**,***

*Division of Artificial Systems Engineering, Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

**Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo 163-8522, Japan

***Department of Neurosurgery, Tokyo Women’s Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo 163-8522, Japan

February 29, 2012
July 4, 2012
October 20, 2012
surgical navigation, neurosurgery, workflow analysis, Bayesian estimation
It has been acknowledged as a problem in recent years that surgery has become complex due to medical system updating. To respond to the increasing demand for making surgery more optimal and efficient, studies on surgical process analysis have attracted attention. Automatic estimation technology is necessary for accurate and efficient process analysis. With a focus on this problem, we have studied technologies on the automatic estimation of surgical processes. In this study, we develop an automatic estimationmethod for a chosen surgical process on the basis of information obtained from a surgical navigation system, taking as an example image-guided brain tumor surgery. We found a significant correlation among five parameters – progress in enucleation, depth of surgical tool tip, displacement of surgical tool, volume of surgical tool position log data, and number of events detected during surgery – that are defined according to the anatomical information on patients and surgical procedure information on surgeons stored in the navigation system, and three stages in the brain tumor removal process: (1) incision of the surface cortex, (2) testing and blood vessel resection, (3) resection and removal of tumors. By using automatic Bayesian estimation of tumor removal processes in eight case examples using the five parameters, we estimated 73% of all processes correctly. This result indicates that surgical processes are automatically estimated with information in the surgical navigation system alone, which thus contributes to the accurate and efficient surgery analysis.
Cite this article as:
R. Nakamura, T. Aizawa, Y. Muragaki, T. Maruyama, and H. Iseki, “Automatic Surgical Workflow Estimation Method for Brain Tumor Resection Using Surgical Navigation Information,” J. Robot. Mechatron., Vol.24 No.5, pp. 791-801, 2012.
Data files:
  1. [1] T. Neumuth, P. Jannin et al., “Analysis of surgical intervention populations using generic surgical process models,” Int. J. of computer assisted radiology and surgery, Vol.6, No.1, pp. 59-71, 2011.
  2. [2] A. Krauss, O. J. Muensterer et al., “Workflow analysis of laparoscopic Nissen fundoplication in infant pigs – A model for surgical feedback and training,” Journal of laparoendoscopic & advanced surgical techniques, Vol.19, Suppl. 1, pp. s117-s122, 2009.
  3. [3] N. Padoy, T. Blum et al., “Statistical modeling and recognition of surgical workflow,” Medical image analysis, 2010.
  4. [4] L. Bouarfa, P. Jonker, and J. Dankelman, “Discovery of high-level tasks in the operating room,” J. of biomedical informatics, Vol.44, No.3, pp. 455-462, 2011.
  5. [5] A James, D Vieira et al., “Eye-Gaze Driven Surgical Workflow Segmentation,” Proc. of Med Image Comput Comput Assist Interv, pp. 110-117, 2007.
  6. [6] B. Bhatia, T. Oates et al., “Real-time identification of operating room state from video,” Proc. of Innovative Applications of Artificial Intelligence, pp. 1761-1766, 2007.
  7. [7] T. Aizawa, R. Nakamura, T. Maruyama, Y. Muragaki, and H. Iseki, “Method to estimate the end time of brain tumor resection by using surgical navigation information,” Int. J. of CARS, Vol.6, Suppl. 1, pp. S141-S142, 2011.
  8. [8] H. Iseki, R. Nakamura, Y. Muragaki, T. Suzuki, M. Chernov, T. Hori, and K. Takakura, “Advanced computer-aided intraoperative technologies for information-guided surgical management of gliomas: Tokyo Women’s Medical University experience,” Minim Invasive Neurosurg, Vol.515, pp. 285-291, 2008.
  9. [9] Y. Muragaki, “Neurosurgical Technique: Brain Tumor,” Currently Practical Neurosugery, Vol.199, pp. 764-769, 2009 (in Japanese).

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 19, 2024