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Advancing medical research with 3D shape analysis of bioimaging data

Tuesday, May 19, 2020 - 4:30pm

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.