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 two biomedical analyses that require shape learning on small imaging datasets: (1) surgical planning for orthopedic surgery, and (2) research on pre-symptomatic biomarkers of Alzheimer's disease. We introduce elements of shape statistics to assess the accuracy of these studies. Then, we address a shape reconstruction challenge in pharmacological research: protein shape reconstruction using cryo-electron microscopy.
This talk shows how shape descriptors at different scales contribute to the development of precision medicine. The elements of geometric statistics required for this work are implemented in the open-source Python library Geomstats.