Image-Guided Procedures, Robotic Interventions, and Modeling

Resection planning for robotic acoustic neuroma surgery

[+] Author Affiliations
Kepra L. McBrayer, Benoit M. Dawant, Jack H. Noble

Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States

George B. Wanna

Vanderbilt University Medical Center, Department of Otolaryngology, Head and Neck Surgery, Nashville, Tennessee, United States

Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States

Ramya Balachandran, Robert F. Labadie

Vanderbilt University Medical Center, Department of Otolaryngology, Head and Neck Surgery, Nashville, Tennessee, United States

J. Med. Imag. 4(2), 025002 (Jun 05, 2017). doi:10.1117/1.JMI.4.2.025002
History: Received July 27, 2016; Accepted May 12, 2017
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Abstract.  Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR’s drilling region using preoperative CT requires expertise and takes about 35 min. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from nine subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 min were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon’s planning effort.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Kepra L. McBrayer ; George B. Wanna ; Benoit M. Dawant ; Ramya Balachandran ; Robert F. Labadie, et al.
"Resection planning for robotic acoustic neuroma surgery", J. Med. Imag. 4(2), 025002 (Jun 05, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.025002


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