Improving whole-brain segmentations through incorporating regional image intensity statistics

Christian Ledig; Rolf A. Heckemann; Alexander Hammers; Daniel Rueckert

doi:10.1117/12.2006966

13 March 2013 Improving whole-brain segmentations through incorporating regional image intensity statistics

Christian Ledig, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691M (2013) https://doi.org/10.1117/12.2006966
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Christian Ledig, Rolf A. Heckemann, Alexander Hammers, and Daniel Rueckert "Improving whole-brain segmentations through incorporating regional image intensity statistics", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691M (13 March 2013); https://doi.org/10.1117/12.2006966

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