Abstract
The rapid development in science and technology in recent years has led to corresponding developments in medical technology. At the same time, however, surgical treatments and procedures are complicated by a variety of techniques and applications of medical devices, which has led to an increase in the demands on surgical staff. To address this problem, surgical workflow analyses have been carried out to ensure the reliability of surgical techniques by visualizing and analyzing complex surgery processes. In previous studies, we visualized the removal progress of malignant brain tumors during MRI-guided navigation surgery and developed a technique to predict the time required for tumor removal using analysis of surgical navigation information. In this paper, we introduce a new method and the results of the end time prediction of tumor removal using both intraoperative performance measurements with surgical navigation data and the estimated mean incremental speed of progress using regression analysis of 20 brain tumor resection cases based on the relationship between tumor size (volume or surface direction) and removal time. The results show that the accuracy was significantly improved from the estimation based on regression analysis by adding the intraoperative progress analysis.
| Original language | English |
|---|---|
| Title of host publication | IFMBE Proceedings |
| Pages | 1452-1455 |
| Number of pages | 4 |
| Volume | 39 IFMBE |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | World Congress on Medical Physics and Biomedical Engineering - Beijing Duration: 2012 May 26 → 2012 May 31 |
Other
| Other | World Congress on Medical Physics and Biomedical Engineering |
|---|---|
| City | Beijing |
| Period | 12/5/26 → 12/5/31 |
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Keywords
- Glioma
- Intraoperative MRI
- Neurosurgery
- Segmentation
- Surgical workflow analysis
ASJC Scopus subject areas
- Biomedical Engineering
- Bioengineering
Cite this
Method for end time prediction of brain tumor resections using analysis of surgical navigation information and tumor size characteristics. / Nakamura, R.; Aizawa, T.; Muragaki, Y.; Maruyama, T.; Iseki, Hiroshi.
IFMBE Proceedings. Vol. 39 IFMBE 2013. p. 1452-1455.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Method for end time prediction of brain tumor resections using analysis of surgical navigation information and tumor size characteristics
AU - Nakamura, R.
AU - Aizawa, T.
AU - Muragaki, Y.
AU - Maruyama, T.
AU - Iseki, Hiroshi
PY - 2013
Y1 - 2013
N2 - The rapid development in science and technology in recent years has led to corresponding developments in medical technology. At the same time, however, surgical treatments and procedures are complicated by a variety of techniques and applications of medical devices, which has led to an increase in the demands on surgical staff. To address this problem, surgical workflow analyses have been carried out to ensure the reliability of surgical techniques by visualizing and analyzing complex surgery processes. In previous studies, we visualized the removal progress of malignant brain tumors during MRI-guided navigation surgery and developed a technique to predict the time required for tumor removal using analysis of surgical navigation information. In this paper, we introduce a new method and the results of the end time prediction of tumor removal using both intraoperative performance measurements with surgical navigation data and the estimated mean incremental speed of progress using regression analysis of 20 brain tumor resection cases based on the relationship between tumor size (volume or surface direction) and removal time. The results show that the accuracy was significantly improved from the estimation based on regression analysis by adding the intraoperative progress analysis.
AB - The rapid development in science and technology in recent years has led to corresponding developments in medical technology. At the same time, however, surgical treatments and procedures are complicated by a variety of techniques and applications of medical devices, which has led to an increase in the demands on surgical staff. To address this problem, surgical workflow analyses have been carried out to ensure the reliability of surgical techniques by visualizing and analyzing complex surgery processes. In previous studies, we visualized the removal progress of malignant brain tumors during MRI-guided navigation surgery and developed a technique to predict the time required for tumor removal using analysis of surgical navigation information. In this paper, we introduce a new method and the results of the end time prediction of tumor removal using both intraoperative performance measurements with surgical navigation data and the estimated mean incremental speed of progress using regression analysis of 20 brain tumor resection cases based on the relationship between tumor size (volume or surface direction) and removal time. The results show that the accuracy was significantly improved from the estimation based on regression analysis by adding the intraoperative progress analysis.
KW - Glioma
KW - Intraoperative MRI
KW - Neurosurgery
KW - Segmentation
KW - Surgical workflow analysis
UR - http://www.scopus.com/inward/record.url?scp=84876062941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876062941&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-29305-4_382
DO - 10.1007/978-3-642-29305-4_382
M3 - Conference contribution
AN - SCOPUS:84876062941
SN - 9783642293047
VL - 39 IFMBE
SP - 1452
EP - 1455
BT - IFMBE Proceedings
ER -