Computational Anatomy of the Brain
Abstract. Understanding and modelling the individual anatomy of the brain and its variability across a population is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. This also raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. As illustrated in Section 1, applications are very important both in neuroscience, to minimise the influence of the anatomical variability in functional group analyses, and in medical imaging, to better drive the adaptation of generic models of the anatomy (atlas) into patient-specific data. Typical examples in the Health-e-Child context are given by tumours growth model which need to be fed with the fibre orientation everywhere in the brain. We present in Section 2 to 4 the methods that we developed in the context of the Health-e-Child project to analyse the morphometry of the cortex from sulcal lines, fibers and surfaces of internal brain structures. One of our specific goal was also to cope with the inherent specificity of paediatric data with the evolution due to growth: Section 5 describes in detail our strategy to analyse the variability both in space and time of the brain maturation across a population.
- Computational Anatomy: Aims and Methods
- Morphometry of the Cortex Inferred from Sulcal Lines
- A Statistical Framework for Anatomical Curves and Surfaces
- A Generative Model of Brain Populations Variability
- Spatiotemporal Atlas Estimation in Longitudinal Datasets
- Example use of Computational Anatomic Models in the Clinical Workflow