BackgroundStudies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.MethodologyTo this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies.ConclusionsWe expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.

Groupwise registration has become more and more popular due to its attractiveness for unbiased analysis of population data. One of the most popular approaches for groupwise registration is to iteratively calculate the group mean image and then register all subject images towards the latest estimated group mean image. However, its performance might be undermined by the fuzzy mean image estimated in the very beginning of groupwise registration procedure, because all subject images are far from being well-aligned at that moment. In this paper, we first point out the significance of always keeping the group mean image sharp and clear throughout the entire groupwise registration procedure, which is intuitively important but has not been explored in the literature yet. To achieve this, we resort to developing the robust mean-image estimator by the adaptive weighting strategy, where the weights are adaptive across not only the individual subject images but also all spatial locations in the image domain. On the other hand, we notice that some subjects might have large anatomical variations from the group mean image, which challenges most of the state-of-the-art registration algorithms. To ensure good registration results in each iteration, we explore the manifold of subject images and build a minimal spanning tree (MST) with the group mean image as the root of the MST. Therefore, each subject image is only registered to its parent node often with similar shapes, and its overall transformation to the group mean image space is obtained by concatenating all deformations along the paths connecting itself to the root of the MST (the group mean image). As a result, all the subjects will be well aligned to the group mean image adaptively. Our method has been evaluated in both real and simulated datasets. In all experiments, our method outperforms the conventional algorithm which generally produces a fuzzy group mean image throughout the entire groupwise registration.

In this paper, we present a multi-atlas-based framework for accurate, consistent and simultaneous segmentation of a group of target images. Multi-atlas-based segmentation algorithms consider concurrently complementary information from multiple atlases to produce optimal segmentation outcomes. However, the accuracy of these algorithms relies heavily on the precise alignment of the atlases with the target image. In particular, the commonly used pairwise registration may result in inaccurate alignment especially between images with large shape differences. Additionally, when segmenting a group of target images, most current methods consider these images independently with disregard of their correlation, thus resulting in inconsistent segmentations of the same structures across different target images. We propose two novel strategies to address these limitations: 1) a novel tree-based groupwise registration method for concurrent alignment of both the atlases and the target images, and 2) an iterative groupwise segmentation method for simultaneous consideration of segmentation information propagated from all available images, including the atlases and other newly segmented target images. Evaluation based on various datasets indicates that the proposed multi-atlas-based multi-image segmentation (MABMIS) framework yields substantial improvements in terms of consistency and accuracy over methods that do not consider the group of target images holistically.

To achieve more accurate and consistent registration in an image population, a novel hierarchical groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this new framework, the global structure, i.e., the relative distribution of subject images is always preserved during the registration process by constraining each subject image to deform only locally with respect to its neighbors within the learned image manifold. To achieve this goal, two novel strategies, i.e., the self-organized registration by warping one image towards a set of its eligible neighbors and image bundling to cluster similar images, are specially proposed. By using these two strategies, this new framework can perform groupwise registration in a hierarchical way. Specifically, in the high level, it will perform on a much smaller dataset formed by the representative subject images of all subgroups that are generated in the previous levels of registration. Compared to the other groupwise registration methods, our proposed framework has several advantages: 1) It explores the local data distribution and uses the obtained distribution information to guide the registration; 2) The possible registration error can be greatly reduced by requiring each individual subject to move only towards its nearby subjects with similar structures; 3) It can produce a smoother registration path, in general, from each subject image to the final built atlas than other groupwise registration methods. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise registration methods, in terms of both registration accuracy and robustness.

Support Vector Machines (SVM)