Share Email Print
cover

Proceedings Paper

Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors
Author(s): Mengyuan Liu; Sharmishtaa Seshamani; Lisa Harrylock; Averi Kitsch; Steven Miller; Van Chau; Kenneth Poskitt; Francois Rousseau; Colin Studholme
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

Paper Details

Date Published: 21 March 2014
PDF: 10 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341H (21 March 2014); doi: 10.1117/12.2042426
Show Author Affiliations
Mengyuan Liu, Univ. of Washington (United States)
Sharmishtaa Seshamani, Univ. of Washington (United States)
Lisa Harrylock, Univ. of Washington (United States)
Averi Kitsch, Univ. of Washington (United States)
Steven Miller, The Hospital for Sick Children (Canada)
Van Chau, The Hospital for Sick Children (Canada)
Kenneth Poskitt, Child & Family Research Institute (Canada)
Francois Rousseau, ICube, CNRS, Univ. de Strasbourg (France)
Colin Studholme, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

© SPIE. Terms of Use
Back to Top