Authors’ reply to: Comment on the article by Dr. Georgios P. Skandalakis: Patient-specific virtual and mixed reality for immersive, experiential anatomy education and for surgical planning in temporal bone surgery

2022 ◽  
Author(s):  
Taku Ito
2016 ◽  
Vol 21 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Sonny Chan ◽  
Peter Li ◽  
Garrett Locketz ◽  
Kenneth Salisbury ◽  
Nikolas H. Blevins

2020 ◽  
Vol 15 (11) ◽  
pp. 1825-1833
Author(s):  
Johannes Fauser ◽  
Simon Bohlender ◽  
Igor Stenin ◽  
Julia Kristin ◽  
Thomas Klenzner ◽  
...  

Abstract Purpose Robot-assisted surgery at the temporal bone utilizing a flexible drilling unit would allow safer access to clinical targets such as the cochlea or the internal auditory canal by navigating along nonlinear trajectories. One key sub-step for clinical realization of such a procedure is automated preoperative surgical planning that incorporates both segmentation of risk structures and optimized trajectory planning. Methods We automatically segment risk structures using 3D U-Nets with probabilistic active shape models. For nonlinear trajectory planning, we adapt bidirectional rapidly exploring random trees on Bézier Splines followed by sequential convex optimization. Functional evaluation, assessing segmentation quality based on the subsequent trajectory planning step, shows the suitability of our novel segmentation approach for this two-step preoperative pipeline. Results Based on 24 data sets of the temporal bone, we perform a functional evaluation of preoperative surgical planning. Our experiments show that the automated segmentation provides safe and coherent surface models that can be used in collision detection during motion planning. The source code of the algorithms will be made publicly available. Conclusion Optimized trajectory planning based on shape regularized segmentation leads to safe access canals for temporal bone surgery. Functional evaluation shows the promising results for both 3D U-Net and Bézier Spline trajectories.


2020 ◽  
pp. 000348942097021
Author(s):  
Steven Arild Wuyts Andersen ◽  
Maxwell Bergman ◽  
Jason P. Keith ◽  
Kimerly A. Powell ◽  
Brad Hittle ◽  
...  

Objectives: Virtual reality (VR) simulation for patient-specific pre-surgical planning and rehearsal requires accurate segmentation of key surgical landmark structures such as the facial nerve, ossicles, and cochlea. The aim of this study was to explore different approaches to segmentation of temporal bone surgical anatomy for patient-specific VR simulation. Methods: De-identified, clinical computed tomography imaging of 9 pediatric patients aged 3 months to 12 years were obtained retrospectively. The patients represented normal anatomy and key structures were manually segmented using open source software. The OTOPLAN (CAScination AG, Bern, Switzerland) otological planning software was used for guided segmentation. An atlas-based algorithm was used for computerized, automated segmentation. Experience with the different approaches as well as time and resulting models were compared. Results: Manual segmentation was time consuming but also the most flexible. The OTOPLAN software is not designed specifically for our purpose and therefore the number of structures that can be segmented is limited, there was some user-to-user variation as well as volume differences compared with manual segmentation. The atlas-based automated segmentation potentially allows a full range of structures to be segmented and produces segmentations comparable to those of manual segmentation with a processing time that is acceptable because of the minimal user interaction. Conclusion: Segmentation is fundamental for patient-specific VR simulation for pre-surgical planning and rehearsal in temporal bone surgery. The automated segmentation algorithm currently offers the most flexible and feasible approach and should be implemented. Further research is needed in relation to cases of abnormal anatomy. Level of evidence: 4


2006 ◽  
Vol 135 (2_suppl) ◽  
pp. P163-P163
Author(s):  
Takechiyo Yamada ◽  
Shigehara Fujiecki ◽  
Yoshimasa Imoto ◽  
Yumi Ito ◽  
Norihiko Nanta ◽  
...  

ORL ◽  
2003 ◽  
Vol 65 (2) ◽  
pp. 71-75 ◽  
Author(s):  
Spiros Manolidis ◽  
Bobby Williamson ◽  
Ling-Ling Chan ◽  
Katherine H. Taber ◽  
L. Anne Hayman

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