Modelling morphological variability of the hippocampus using manifold learning and large deformations

International audienceThe hippocampus is a structure of the temporal lobe of the brain which plays an important role in memory processes and in many neuropsychiatric disorders, including Alzheimer's disease, epilepsy and schizophrenia. Here, we present a method to infer the anatomical variability of the hippocampus, based on the LDDMM (Large Deformation Diffeomorphic Metric Mapping) framework and a manifold learning approach to capture the geometry of the population in the space of shapes

Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

International audienceA common approach for analysis of anatomical variability relies on the stimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This paper presents an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach is evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results show that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects

Approximations of the diffeomorphic metric and their applications in shape learning.

Information processing in medical imaging : proceedings of the ... conference22257-27