Surgical Process Identification System in Awake Surgery for Glioma
Author:
Affiliation:
1. Graduate School of Systems Information Science, Future University Hakodate
2. Institute of Advanced Biomedical Engineering and Science, Tokyo Women’s Medical University
Publisher
The Japan Society of Computer Aided Surgery
Link
https://www.jstage.jst.go.jp/article/jscas/22/2/22_87/_pdf
Reference18 articles.
1. 1) Nitta M, Muragaki Y, Maruyama T, et al. Proposed therapeutic strategy for adult low-grade glioma based on aggressive tumor resection. Neurosurg Focus 2015; 38 (1) E7-E10.
2. 2) Iseki H, Nakamura R, Muragaki Y, et al. Advanced computer-aided intraoperative technologies for information-guided surgical management of gliomas: Tokyo Women’s Medical University Experience. Minimally Invasive Neurosurgery 2008; 51 (5); 285-291.
3. 3) Hong J, Muragaki Y, Nakamura R, et al. A neurosurgical navigation system based on intraoperative tumor remnant estimation 2007; 1 (1): 91-97.
4. 5) Muragaki Y, Iseki H, Maruyama T, et al. nformation-guided surgical management of gliomas using low-field-strength intraoperative MRI. Intraoperative Imaging 2011; 109: 67-72.
5. 8) Aksamentov I, Twinanda AP, Mutter D, et al. neural networks predict remaining surgery duration from cholecystectomy videos. Medical Image Computing and Computer-Assisted Intervention- MICCAI 2017. 2017; 10434: 586-593.
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