Share Email Print
cover

Proceedings Paper

Automated tissue classification of pediatric brains from magnetic resonance images using age-specific atlases
Author(s): Andrew Metzger; Amanda Benavides; Peg Nopoulos; Vincent Magnotta
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

Paper Details

Date Published: 29 March 2016
PDF: 7 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978820 (29 March 2016); doi: 10.1117/12.2216116
Show Author Affiliations
Andrew Metzger, Univ. of Iowa (United States)
Amanda Benavides, Univ. of Iowa (United States)
Peg Nopoulos, Univ. of Iowa (United States)
Vincent Magnotta, Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

© SPIE. Terms of Use
Back to Top