Paper
17 February 2012 A surgeon specific automatic path planning algorithm for deep brain stimulation
Author Affiliations +
Abstract
In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that parameters, which potentially can be automatically learned, are used to describe an individual surgeon's preferences. To validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path planning.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Liu, Benoit M. Dawant, Srivatsan Pallavaram, Joseph S. Neimat, Peter E. Konrad, Pierre-Francois D'Haese, Ryan D. Datteri, Bennett A. Landman, and Jack H. Noble "A surgeon specific automatic path planning algorithm for deep brain stimulation", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83161D (17 February 2012); https://doi.org/10.1117/12.911705
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CITATIONS
Cited by 13 scholarly publications.
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KEYWORDS
Surgery

Brain stimulation

Brain

Thalamus

Magnetic resonance imaging

Electrodes

Image segmentation

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