Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a "macroscopic" growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns.

Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a "macroscopic" growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns.

Cette thèse présente une méthodologie appliquée à la caractérisation de la dynamique de structures déformables sur des séquences temporelles (2D+t). Des indicateurs sont proposés pour estimer la mobilité de formes non-rigides, à partir de leurs contours. Deux approches complémentaires sont développées: En premier lieu, les descripteurs de forme sont utilisés pour quantifier les déformations globales des formes, et pour estimer des repères géométriques spécifiques. La deuxième approche repose sur l'appariement difféomorphique pour déterminer une paramétrisation unifiée des formes, afin de décrire les déformations. Une évaluation permet d'apprécier la qualité des indicateurs en termes de coût algorithmique, de robustesse face aux données altérées, et de capacité à différencier deux séquences.Cette approche de caractérisation est appliquée à des séquences IRM dynamiques de la cavité pelvienne, où les principaux organes pelviens (vessie, utérus-vagin, rectum) ont une grande variabilité morphologique, ils se déplacent et se déforment. Cette caractérisation est validée dans le cadre de deux applications. L'analyse statistique effectuée sur un ensemble de séquences permet de mettre en évidence des comportements caractéristiques des organes, d'identifier des références anatomiquement significatives, et d'aider à l'interprétation des diagnostics des organes. Aussi, dans le contexte de la réalisation d'une modélisation de la dynamique pelvienne patiente-spécifique, la caractérisation vise à évaluer quantitativement la précision de la modélisation, en utilisant l'IRM dynamique comme vérité-terrain. Ainsi, elle apporte des indications sur la correction des paramètres du modèle. / This thesis presents a methodology for the characterization of the dynamics of deformable structures on time-series data (2D+t). Some indicators are proposed in order to estimate non-rigid shape variations from their contours. Two complementary approaches are developed : First, shape descriptors are used to quantify the global deformations of the shapes, and to estimate specific geometric references. The second approach relies on the diffeomorphic mapping to determinate a unified parametrization of the shapes. Then, features are used to describe the deformations locally. Furthermore, the methodology has an evaluation step which consists in the assessment of the quality of the indicators in the algorithmic complexity, in the stability against data with a small variability, and in the ability to differentiate two sequences.The characterization is applied to dynamic MRI sequences of the pelvic cavity, where the main pelvic organs (bladder, uterus-vagina, rectum) have a high morphological variability, they undergo displacements and deformations. The characterization is validated within the context of two applications. Firslty, a statistical analysis is carried out on a set of sequences. It allows to highlight some properties of the organ behaviors, and to identify meaningful anatomical landmarks. The analysis helps also for the automatic interpretation of the organ diagnoses. Secondly, within the context of the development of a patient-specific pelvic dynamics modeling system, the characterization aims at assessing quantitatively the modeling precision. It uses the dynamic MRI as a ground truth. Thereby, it brings some clues about the correction of the model parameters.

Natural surfaces are usually associated with feature graphs, such as the cortical surface with anatomical atlas structure. Such a feature graph subdivides the whole surface into meaningful sub-regions. Existing brain mapping and registration methods did not integrate anatomical atlas structures. As a result, with existing brain mappings, it is difficult to visualize and compare the atlas structures. And also existing brain registration methods can not guarantee the best possible alignment of the cortical regions which can help computing more accurate shape similarity metrics for neurodegenerative disease analysis, e.g., Alzheimer’s disease (AD) classification. Also, not much attention has been paid to tackle surface parameterization and registration with graph constraints in a rigorous way which have many applications in graphics, e.g., surface and image morphing. This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints. (1) First, we propose structural brain mapping which maps the brain cortical surface onto a planar convex domain using Tutte embedding of a novel atlas graph and harmonic map with atlas graph constraints to facilitate visualization and comparison between the atlas structures. (2) Next, we propose a novel brain registration technique based on an intrinsic atlas-constrained harmonic map which provides the best possible alignment of the cortical regions. (3) After that, the proposed brain registration technique has been applied to compute shape similarity metrics for AD classification. (4) Finally, we propose techniques to compute intrinsic graph-constrained parameterization and registration for general genus-0 surfaces which have been used in surface and image morphing applications.