Statistical Shape Analysis: Theory, Software, and Applications
Presented by
Presented by Tom Fletcher, University of Utah; Ivo Dinov, University of California, Los Angeles; Polina Golland, Massachusetts Institute of Technology; Shantanu Joshi, Jon Morra, Yonggang Shi, Vishal Patel, University of California, Los Angeles
Abstract
Statistical shape analysis is an important tool for understanding biological and anatomical structures from image data. The study of biological shape can give us insights into disease processes as well as normal growth and development. The theory of shape has a rich mathematical underpinning, including the theory of shape manifolds and diffeomorphic transformations. This tutorial will be aimed at researchers in shape analysis methodology as well as clinical researchers interested in applying these methods in applications.
This tutorial will cover the following topics:
- Shape Theory
- Mathematics of shape spaces
- Statistics in nonlinear shape spaces
- Shape from image warping
- Testing hypotheses about shape
- Shape Analysis Software: Current tools, publicly avaliable as part of the NAMIC and CCB projects, will be presented.
- Clinical Applications: Applications to various clinical studies will be presented, including autism, Alzheimer's disease, healthy aging studies, and more.