SPEAKER: Nina Miolane, Postdoctoral Fellow, Statistics, Stanford University
TITLE: “Geometric statistics for shape analysis of bioimaging data”
ABSTRACT: The advances in bioimaging techniques have enabled us to access the 3D shapes of a variety of structures: organs, cells, proteins. Since biological shapes are related to physiological functions, statistical analyses in biomedical research are poised to incorporate more shape data. This leads to the question: how do we define quantitative descriptions of shape variability from images? In neuroimaging, for example, how can we model healthy and pathological variations of brain shapes?
Mathematically, landmarks’ shapes, curve shapes, or surface shapes can be seen as the remainder after we have filtered out the corresponding object’s position and orientation. As such, shape data belong to quotient spaces, which are non-Euclidean spaces. In this talk, I will introduce “Geometric statistics”, a statistical theory for data belonging to non-Euclidean spaces, and present associated open-source implementations. In the context of shape data analysis, I will use geometric statistics to prove that the “template shape estimation” algorithm, used for more than 15 years in medical imaging and signal processing, has an asymptotic bias. I will present experimental results on biomedical analyses of optic nerve shapes and brain shapes. Finally, I will discuss the use of geometric statistics and shape statistics to design fast yet reliable methods of biomedical shape variability quantification.
DATE: Wednesday, January 8th, 10:30am
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS at 10:30am in MSB 1147, seminar to begin at 10:40am