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Proceedings Paper

Automatic brain segmentation in rhesus monkeys
Author(s): Martin Styner; Rebecca Knickmeyer; Sarang Joshi; Christopher Coe; Sarah J. Short; John Gilmore
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Paper Abstract

Many neuroimaging studies are applied to primates as pathologies and environmental exposures can be studied in well-controlled settings and environment. In this work, we present a framework for both the semi-automatic creation of a rhesus monkey atlas and a fully automatic segmentation of brain tissue and lobar parcellation. We determine the atlas from training images by iterative, joint deformable registration into an unbiased average image. On this atlas, probabilistic tissue maps and a lobar parcellation. The atlas is then applied via affine, followed by deformable registration. The affinely transformed atlas is employed for a joint T1/T2 based tissue classification. The deformed atlas parcellation masks the tissue segmentations to define the parcellation. Other regional definitions on the atlas can also straightforwardly be used as segmentation. We successfully built average atlas images for the T1 and T2 datasets using a developmental training datasets of 18 cases aged 16-34 months. The atlas clearly exhibits an enhanced signal-to-noise ratio compared to the original images. The results further show that the cortical folding variability in our data is highly limited. Our segmentation and parcellation procedure was successfully re-applied to all training images, as well as applied to over 100 additional images. The deformable registration was able to identify corresponding cortical sulcal borders accurately. Even though the individual methods used in this segmentation framework have been applied before on human data, their combination is novel, as is their adaptation and application to rhesus monkey MRI data. The reduced variability present in the primate data results in a segmentation pipeline that exhibits high stability and anatomical accuracy.

Paper Details

Date Published: 8 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122L (8 March 2007); doi: 10.1117/12.710027
Show Author Affiliations
Martin Styner, Univ. of North Carolina at Chapel Hill (United States)
Rebecca Knickmeyer, Univ. of North Carolina at Chapel Hill (United States)
Sarang Joshi, Univ. of Utah (United States)
Christopher Coe, Univ. of Wisconsin/Madison (United States)
Sarah J. Short, Univ. of Wisconsin/Madison (United States)
John Gilmore, Univ. of North Carolina at Chapel Hill (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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