Speaker: Nina Miolane, Stanford Statistics
Abstract: 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, medical research is poised to incorporate more shape statistics. This leads to the question: how can we build quantified descriptions of shape variability from biomedical images?
We first consider a biomedical analysis that requires statistics on landmarks' shapes: the automatic diagnosis of glaucoma from ophthalmoscopy. We introduce elements of shape statistics to assess the accuracy of this study. Then, we address a shape reconstruction challenge in structural biology: molecular shape reconstruction using cryo-electron microscopy.
This talk shows how shape descriptors at different scales contribute to advancing computational biomedicine. The elements of geometric statistics required for this work are implemented in the open-source Python library Geomstats.