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On Exploiting Connectomics for Thalamic Nuclei Localization: A Supervised Learning Approach

Dimitris Bolis, Andras Jakab, Orcun Goksel, Gabor Székely
International Conference on Machine Learning - ICML Workshop on Statistics, Machine Learning and Neuroscience
Edinbugh, Scotland, July 2012

Abstract

We hypothesize that thalamocortical connections after mapping by diffusion tensor imaging can serve as surrogate markers of individual anatomy, which can then be used for localizing specific neurosurgical targets in the thalamus. A variety of learning schemes, together with pre- and post- processing steps are studied for our goal of thalamic nuclei localization. The training procedure is performed after non-linear registration and probabilistic tractography on diffusion magnetic resonance imaging data. Our results indicate that thalamocortical connectivity data do contain sufficient discriminant internucleus information for thalamic nuclei localization.


Link to publisher's page
@InProceedings{eth_biwi_00939,
  author = {Dimitris Bolis and Andras Jakab and Orcun Goksel and Gabor Székely},
  title = {On Exploiting Connectomics for Thalamic Nuclei Localization: A Supervised Learning Approach},
  booktitle = {International Conference on Machine Learning - ICML Workshop on Statistics, Machine Learning and Neuroscience},
  year = {2012},
  month = {July},
  keywords = {}
}