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Proceedings Paper

Predictive modeling of neuroanatomic structures for brain atrophy detection
Author(s): Xintao Hu; Lei Guo; Jingxin Nie; Kaiming Li; Tianming Liu
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Paper Abstract

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241K (9 March 2010); doi: 10.1117/12.843901
Show Author Affiliations
Xintao Hu, Northwestern Polytechnical Univ. (China)
Lei Guo, Northwestern Polytechnical Univ. (China)
Jingxin Nie, Northwestern Polytechnical Univ. (China)
Kaiming Li, Northwestern Polytechnical Univ. (China)
The Univ. of Georgia (United States)
Tianming Liu, The Univ. of Georgia (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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