Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set

Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness

Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set

Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness

Groupwise registration with global-local graph shrinkage in atlas construction

Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph. The final atlas is a mean image at the population center of the distribution on the manifold. The procedure of warping all images to the mean image turns to dynamic graph shrinkage in which nodes become closer to each other. Most conventional groupwise registration frameworks construct and shrink a graph without considering the local distribution of images on the dataset manifold and the local structure variations between image pairs. Neglecting the local information fundamentally decrease the accuracy and efficiency when population atlases are built for organs with large inter-subject anatomical variabilities. To overcome the problem, this paper proposes a global-local graph shrinkage approach that can generate accurate atlas. A connected graph is constructed automatically based on global similarities across the images to explore the global distribution. A local image distribution obtained by image clustering is used to simplify the edges of the constructed graph. Subsequently, local image similarities refine the deformation estimated through global image similarity for each image warping along the graph edges. Through the image warping, the overall simplified graph shrinks gradually to yield the atlas with respecting both global and local features. The proposed method is evaluated on 61 synthetic and 20 clinical liver datasets, and the results are compared with those of six state-of-the-art groupwise registration methods. The experimental results show that the proposed method outperforms non-global-local method approaches in terms of accuracy

Registration of brain MR images in large-scale populations

Non-rigid image registration is fundamentally important in analyzing large-scale population of medical images, e.g., T1-weighted brain MRI data. Conventional pairwise registration methods involve only two images, as the moving subject image is deformed towards the space of the template for the maximization of their in-between similarity. The population information, however, is mostly ignored, with individual images in the population registered independently with the arbitrarily selected template. By contrast, this dissertation investigates the contributions of the entire population to image registration. First, the population can provide guidance to the pairwise registration between a certain subject and the template. If the subject and an intermediate image in the same population are similar in appearances, the subject shares a similar deformation field with the intermediate image. Thus, the guidance from the intermediate image can be beneficial to the subject, in that the pre-estimated deformation field of the intermediate image initiates the estimation of the subject deformation field when the two images are registered with the identical template. Second, all images in the population can be registered towards the common space of the population using the groupwise technique. Groupwise registration differs from the traditional design of pairwise registration in that no template is pre-determined. Instead, all images agglomerate to the common space of the population simultaneously. Moreover, the common space is revealed spontaneously during image registration, without introducing any bias towards the subsequent analyses and applications. This dissertation shows that population information can contribute to both pairwise registration and groupwise registration. In particular, by utilizing the guidance from the intermediate images in the population, the pairwise registration is more robust and accurate compared to the direct pairwise registration between the subject and the template. Also, for groupwise registration, all images in the population can be aligned more accurately in the common space, although the complexity of groupwise registration increases substantially.Doctor of Philosoph