Improving whole-brain segmentations through incorporating regional image intensity statistics
Ledig C, Heckemann RA, Hammers A, Rueckert D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8669
Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Event location: USA
ISBN: 9780819494436
DOI: 10.1117/12.2006966
Abstract
Multi-atlas segmentation methods are among the most accurate approaches for the automatic labeling of magnetic resonance (MR) brain images. The individual segmentations obtained through multi-atlas propagation can be combined using an unweighted or locally weighted fusion strategy. Label overlaps can be further improved by refining the label sets based on the image intensities using the Expectation-Maximisation (EM) algorithm. A drawback of these approaches is that they do not consider knowledge about the statistical intensity characteristics of a certain anatomical structure, especially its intensity variance. In this work we employ learned characteristics of the intensity distribution in various brain regions to improve on multi-atlas segmentations. Based on the intensity profile within labels in a training set, we estimate a normalized variance error for each structure. The boundaries of a segmented region are then adjusted until its intensity characteristics are corrected for this variance error observed in the training sample. Specifically, we start with a high-probability "core" segmentation of a structure, and maximise the similarity with the expected intensity variance by enlarging it. We applied the method to 35 datasets of the OASIS database for which manual segmentations into 138 regions are available. We assess the resulting segmentations by comparison with this gold-standard, using overlap metrics. Intensity-based statistical correction improved similarity indices (SI) compared with EM-refined multi-atlas propagation from 75.6% to 76.2% on average. We apply our novel correction approach to segmentations obtained through either a locally weighted fusion strategy or an EM-based method and show significantly increased similarity indices. © 2013 SPIE.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Ledig, C., Heckemann, R.A., Hammers, A., & Rueckert, D. (2013). Improving whole-brain segmentations through incorporating regional image intensity statistics. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. USA.
MLA:
Ledig, Christian, et al. "Improving whole-brain segmentations through incorporating regional image intensity statistics." Proceedings of the Medical Imaging 2013: Image Processing, USA 2013.
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