{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T03:00:24Z","timestamp":1775012424221,"version":"3.50.1"},"reference-count":9,"publisher":"Fuji Technology Press Ltd.","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Robot. Mechatron.","JRM"],"published-print":{"date-parts":[[2012,10,20]]},"abstract":"<jats:p>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 \u2013 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 \u2013 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.<\/jats:p>","DOI":"10.20965\/jrm.2012.p0791","type":"journal-article","created":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T02:23:03Z","timestamp":1460600583000},"page":"791-801","source":"Crossref","is-referenced-by-count":10,"title":["Automatic Surgical Workflow Estimation Method for Brain Tumor Resection Using Surgical Navigation Information"],"prefix":"10.20965","volume":"24","author":[{"given":"Ryoichi","family":"Nakamura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Division of Artificial Systems Engineering, Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoaki","family":"Aizawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiro","family":"Muragaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Maruyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Iseki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Institute of Advanced Biomedical Engineering and Science, Tokyo Women\u2019s Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo 163-8522, Japan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Department of Neurosurgery, Tokyo Women\u2019s Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo 163-8522, Japan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2012,10,20]]},"reference":[{"key":"key-10.20965\/jrm.2012.p0791-1","doi-asserted-by":"crossref","unstructured":"T. Neumuth, P. Jannin et al., \u201cAnalysis of surgical intervention populations using generic surgical process models,\u201d Int. J. of computer assisted radiology and surgery, Vol.6, No.1, pp. 59-71, 2011.","DOI":"10.1007\/s11548-010-0475-y"},{"key":"key-10.20965\/jrm.2012.p0791-2","doi-asserted-by":"crossref","unstructured":"A. Krauss, O. J. Muensterer et al., \u201cWorkflow analysis of laparoscopic Nissen fundoplication in infant pigs \u2013 A model for surgical feedback and training,\u201d Journal of laparoendoscopic & advanced surgical techniques, Vol.19, Suppl. 1, pp. s117-s122, 2009.","DOI":"10.1089\/lap.2008.0198.supp"},{"key":"key-10.20965\/jrm.2012.p0791-3","unstructured":"N. Padoy, T. Blum et al., \u201cStatistical modeling and recognition of surgical workflow,\u201d Medical image analysis, 2010."},{"key":"key-10.20965\/jrm.2012.p0791-4","doi-asserted-by":"crossref","unstructured":"L. Bouarfa, P. Jonker, and J. Dankelman, \u201cDiscovery of high-level tasks in the operating room,\u201d J. of biomedical informatics, Vol.44, No.3, pp. 455-462, 2011.","DOI":"10.1016\/j.jbi.2010.01.004"},{"key":"key-10.20965\/jrm.2012.p0791-5","doi-asserted-by":"crossref","unstructured":"A James, D Vieira et al., \u201cEye-Gaze Driven Surgical Workflow Segmentation,\u201d Proc. of Med Image Comput Comput Assist Interv, pp. 110-117, 2007.","DOI":"10.1007\/978-3-540-75759-7_14"},{"key":"key-10.20965\/jrm.2012.p0791-6","unstructured":"B. Bhatia, T. Oates et al., \u201cReal-time identification of operating room state from video,\u201d Proc. of Innovative Applications of Artificial Intelligence, pp. 1761-1766, 2007."},{"key":"key-10.20965\/jrm.2012.p0791-7","unstructured":"T. Aizawa, R. Nakamura, T. Maruyama, Y. Muragaki, and H. Iseki, \u201cMethod to estimate the end time of brain tumor resection by using surgical navigation information,\u201d Int. J. of CARS, Vol.6, Suppl. 1, pp. S141-S142, 2011."},{"key":"key-10.20965\/jrm.2012.p0791-8","doi-asserted-by":"crossref","unstructured":"H. Iseki, R. Nakamura, Y. Muragaki, T. Suzuki, M. Chernov, T. Hori, and K. Takakura, \u201cAdvanced computer-aided intraoperative technologies for information-guided surgical management of gliomas: Tokyo Women\u2019s Medical University experience,\u201d Minim Invasive Neurosurg, Vol.515, pp. 285-291, 2008.","DOI":"10.1055\/s-0028-1082333"},{"key":"key-10.20965\/jrm.2012.p0791-9","unstructured":"Y. Muragaki, \u201cNeurosurgical Technique: Brain Tumor,\u201d Currently Practical Neurosugery, Vol.199, pp. 764-769, 2009 (in Japanese)."}],"container-title":["Journal of Robotics and Mechatronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/phyosetsu.php?ppno=ROBOT002400050008","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,25]],"date-time":"2017-06-25T00:39:38Z","timestamp":1498351178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jrm\/rb\/robot002400050791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,20]]},"references-count":9,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2012,10,20]]},"published-print":{"date-parts":[[2012,10,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jrm.2012.p0791","relation":{},"ISSN":["1883-8049","0915-3942"],"issn-type":[{"value":"1883-8049","type":"electronic"},{"value":"0915-3942","type":"print"}],"subject":[],"published":{"date-parts":[[2012,10,20]]}}}