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Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9784. pii: 97843J. Epub 2016 Mar 21.

Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors.

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  • 1Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
  • 2Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Dept. of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
  • 3Dept. of Computer Science, George Mason University, Fairfax, VA 22030, USA.
  • 4Dept. of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.


Segmentation of the thalamus and thalamic nuclei is useful to quantify volumetric changes from neurodegenerative diseases. Most thalamus segmentation algorithms only use T1-weighted magnetic resonance images and current thalamic parcellation methods require manual interaction. Smaller nuclei, such as the lateral and medial geniculates, are challenging to locate due to their small size. We propose an automated segmentation algorithm using a set of features derived from diffusion tensor image (DTI) and thalamic nuclei location priors. After extracting features, a hierarchical random forest classifier is trained to locate the thalamus. A second random forest classifies thalamus voxels as belonging to one of six thalamic nuclei classes. The proposed algorithm was tested using a leave-one-out cross validation scheme and compared with state-of-the-art algorithms. The proposed algorithm has a higher Dice score compared to other methods for the whole thalamus and several nuclei.


Diffusion MRI; machine learning; magnetic resonance imaging; segmentation; thalamus parcellation

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