Abstract
In surgical treatment of Parkinson’s disease, deep brain stimulation requires high-precision positioning of electrodes, needing accurate localization and outlines of anatomical targets. Manual procedure of anatomical structures outlining on magnetic resonance images (MRI) takes about several hours. We proposed an automated localizing procedure aiming to shorten this task to some seconds. Different parameters were simultaneously assessed in our algorithm undertaking segmentation of anatomical structures. Intraclass correlation coefficients (ICCs) were computed for centers of gravity coordinates of structures between manual expert-mapped MRI and automated-mapped MRI. Tanimoto coefficients were computed accounting for pixels overlapping between these two procedures. Although ICCs showed almost perfect concordance, TC provided further information with a quite severe value about 35%. For both criteria, results were variable regarding each parameter in our process. With such complex results to relate, their presentations were enhanced using visualization methods resembling that of the generalized Case View method.
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Ouchchane, L., Villéger, A., Lemaire, JJ., Demongeot, J., Boire, JY. (2007). Analysis and Visualization of Images Overlapping: Automated Versus Expert Anatomical Mapping in Deep Brain Stimulation Targeting. In: Lévy, P.P., et al. Pixelization Paradigm. VIEW 2006. Lecture Notes in Computer Science, vol 4370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71027-1_12
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DOI: https://doi.org/10.1007/978-3-540-71027-1_12
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