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

Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

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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.


Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.


Dictionary learning and sparse representation; diffusion tensor imaging; segmentation; thalamus

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