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Proceedings Paper

Semi-automatic parcellation of the corpus striatum
Author(s): Ramsey Al-Hakim; Delphine Nain; James Levitt; Martha Shenton; Allen Tannenbaum
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Paper Abstract

The striatum is the input component of the basal ganglia from the cerebral cortex. It includes the caudate, putamen, and nucleus accumbens. Thus, the striatum is an important component in limbic frontal-subcortical circuitry and is believed to be relevant both for reward-guided behaviors and for the expression of psychosis. The dorsal striatum is composed of the caudate and putamen, both of which are further subdivided into pre- and post-commissural components. The ventral striatum (VS) is primarily composed of the nucleus accumbens. The striatum can be functionally divided into three broad regions: 1) a limbic; 2) a cognitive and 3) a sensor-motor region. The approximate corresponding anatomic subregions for these 3 functional regions are: 1) the VS; 2) the pre/post-commissural caudate and the pre-commissural putamen and 3) the post-commissural putamen. We believe assessing these subregions, separately, in disorders with limbic and cognitive impairment such as schizophrenia may yield more informative group differences in comparison with normal controls than prior parcellation strategies of the striatum such as assessing the caudate and putamen. The manual parcellation of the striatum into these subregions is currently defined using certain landmark points and geometric rules. Since identification of these areas is important to clinical research, a reliable and fast parcellation technique is required. Currently, only full manual parcellation using editing software is available; however, this technique is extremely time intensive. Previous work has shown successful application of heuristic rules into a semi-automatic platform1. We present here a semi-automatic algorithm which implements the rules currently used for manual parcellation of the striatum, but requires minimal user input and significantly reduces the time required for parcellation.

Paper Details

Date Published: 5 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651236 (5 March 2007); doi: 10.1117/12.708950
Show Author Affiliations
Ramsey Al-Hakim, Georgia Institute of Technology (United States)
Delphine Nain, Georgia Institute of Technology (United States)
James Levitt, Harvard Medical School (United States)
Martha Shenton, Harvard Medical School (United States)
Allen Tannenbaum, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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