The automated extraction of features from magnetic resonance images (MRI) of the brain is an increasingly important step in neuroimaging. Since the brain anatomy varies significantly across subjects and can undergo significant change, either during aging or through disease progression, finding an appropriate way of dealing with anatomical differences during feature extraction has gained increasing attention in recent years.
Among the most popular methods for dealing with this variability are atlas-based approaches. These approaches assume that the atlases can encode the anatomical variability either in a probabilistic or statistical fashion. When building representative atlases, it is important to register all images to a template that is unbiased towards any particular subgroup of the population (Thompson et al., 2000
). Two approaches using the large deformation diffeomorphic setting for shape averaging and atlas construction have been proposed by Avants and Gee (2004)
and Joshi et al. (2004)
, respectively. Template-free methods for co-registering images form an established framework for spatial image normalization (Studholme and Cardenas, 2004
; Avants and Gee, 2004
; Zöllei et al., 2005
; Lorenzen et al., 2006
; Bhatia et al., 2007
). In a departure from approaches that seek a single representative average atlas, two more recent methods describe ways of identifying the modes of different populations in an image dataset (Blezek and Miller, 2007
; Sabuncu et al., 2008
). To design variable atlases dependent on subject information, a variety of approaches have been applied in recent years to the problem of characterizing anatomical changes in brain shape over time and during disease progression. Davis et al. (2007)
describe a method for population shape regression in which kernel regression is adapted to the manifold of diffeomorphisms and is used to obtain an age-dependent atlas. Ericsson et al. (2008)
propose a method for the construction of a patient-specific atlas where different average brain atlases are built in a small deformation setting according to meta-information such as sex, age, or clinical factors.
Methods for extracting features or biomarkers from MR brain image data often begin by automatically segmenting regions of interest. A very popular segmentation technique is to use label propagation, which transforms labels from an atlas image to an unseen target image by bringing both images into alignment. Atlases are typically, but not necessarily, manually labeled. Early work using this approach was proposed by Bajcsy et al. (1983)
as well as more recently by Gee et al. (1993)
and Collins et al. (1995)
. The accuracy of label propagation strongly depends on the accuracy of the underlying image alignment. To overcome the reliance on a single segmentation, Warfield et al. (2004)
proposed STAPLE, a method that computes for a collection of segmentations a probabilistic estimate of the true segmentation. Rohlfing et al. (2004)
demonstrated the improved robustness and accuracy of a multi-classifier framework where the labels propagated from multiple atlases are combined in a decision fusion step to obtain a final segmentation of the target image. Label propagation in combination with decision fusion was successfully used to segment a large number of structures in brain MR images by Heckemann et al. (2006)
Due to the wide range of anatomical variation, the selection of atlases becomes an important issue in multi-atlas segmentation. The selection of suitable atlases for a given target helps to ensure that the atlas-target registrations and the subsequent segmentation are as accurate as possible. Wu et al. (2007)
describe different methods for improving segmentation results in the single atlas case by incorporating atlas selection. Aljabar et al. (2009)
investigate different similarity measures for optimal atlas selection during multi-atlas segmentation. van Rikxoort et al. (2008)
propose a method where atlas combination is carried out separately in different sub-windows of an image until a convergence criterion is met. These approaches show that it is meaningful to select suitable atlases for each target image individually. Although an increasing number of MR brain images are available, the generation of high-quality manual atlases is a labor-intensive and expensive task (see, e.g., Hammers et al., 2003
). This means that atlases are often relatively limited in number and, in most cases, restricted to a particular population (e.g., young, healthy subjects). This can limit the applicability of the atlas database even if a selection approach is used. To overcome this, Tang et al. (2009)
seek to produce a variety of atlas images by utilizing a PCA model of deformations learned from transformations between a single template image and training images. Potential atlases are generated by transforming the initial template with a number of transformations sampled from the model. The assumption is that, by finding a suitable atlas for an unseen image, a fast and accurate registration to this template may be readily obtained. Test data with a greater level of variation than the training data would, however, represent a significant challenge to this approach. Additionally, the use of a highly variable training dataset may lead to an unrepresentative PCA model as the likelihood of registration errors between the diverse images and the single template is increased. This restriction makes this approach only applicable in cases were a good registration from all training images to the single initial template can be easily obtained.
The approach we follow in this work aims to propagate a relatively small number of atlases through to a large and diverse set of MR brain images exhibiting a significant amount of anatomical variability. The initial atlases may only represent a specific subgroup of target image population, and the method is designed to address this challenge. As previously shown, atlas-based segmentation benefits from the selection of atlases similar to the target image (Wu et al., 2007
; Aljabar et al., 2009
). We propose a framework where this is ensured by first embedding all images in a low-dimensional coordinate system that provides a distance metric between images and allows neighborhoods of images to be identified. In the manifold learned from coordinate system embedding, a propagation framework can be identified and labeled atlases can be propagated in a stepwise fashion, starting with the initial atlases, until the whole population is segmented. Each image is segmented using atlases that are within its neighborhood, meaning that deformations between dissimilar images are broken down to several small deformations between comparatively similar images and registration errors are reduced. To further minimize an accumulation of registration errors, an intensity-based refinement of the segmentation is done after each label propagation step. Once segmented, an image can in turn be used as an atlas in subsequent segmentation steps. After all images in the population are segmented, they represent a large atlas database from which suitable subsets can be selected for the segmentation of unseen images. The coordinate system into which the images are embedded is obtained by applying a spectral analysis step (Chung, 1997
) to their pairwise similarities. As labeled atlases are propagated and fused for a particular target image, the information they provide is combined with a model based on the target image intensities to generate the final segmentation (van der Lijn et al., 2008
; Wolz et al., 2009
Prior work where automatically labeled brain images were used to label unseen images did not result in an improvement of segmentation accuracy over direct multi-atlas propagation. In Heckemann et al. (2006)
, when multiple relatively homogenous atlases were propagated to randomly selected intermediate images that were used as single atlases for the segmentation of unseen images, the resulting average Dice overlaps with manual delineations were 0.80, compared with 0.84 for direct multi-atlas propagation and fusion. In a second experiment, single atlases were propagated to randomly selected intermediate subjects that were then further used for multi-atlas segmentation, resulting in Dice overlaps with manual delineations of 0.78 at best. In this article, however, we use multi-atlas segmentation to systematically label intermediate atlases that are then used for multi-atlas segmentation of target images that are selected according to their similarity with the previously labeled atlas images. Compared to previous work, we are dealing with a very diverse set of images. In such a scenario, the gain from only registering similar images is more likely to outweigh the accumulation of registration errors.
Our initial set of atlases consists of 30 MR images from young and healthy subjects together with manual label maps defining 83 anatomical structures of interest. We used the proposed method to propagate this initial set of atlases to a dataset of 796 MR images acquired from patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) as well as age-matched controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (). We show that this approach provides more accurate segmentations due, at least in part, to the associated reductions in inter-subject registration error.
Information relating to the subjects whose images were used in this study.