Overview

The concept of "shape" has taken an increasingly central place in the biomedical sciences. Historically, the pioneering work of D'Arcy Thompson morphogenesis has introduced the idea of mathematical laws underlying the formation of patterns in plants, animal and human anatomy. With the constant breakthroughs and innovations in biomedical imaging technologies, and the resulting explosion in the availability of high-quality data, the need for sound mathematical and computational models to analyze such data and uncover the underlying biological processes behind it has become all the more crucial. This led to the emergence of novel research areas, such as, in the late 1990s, the field known as computational anatomy, which aims at building models of the anatomical variability and developing quantitative approaches to extract morphological biomarkers associated to pathologies. On the mathematical side, these questions have found natural connections with the construction of shape spaces and the problem of extending statistical methods to such spaces.

This workshop, targeting a broad audience of mathematicians, statisticians, computational biologists and neuroscientists, will be articulated around four main topics: latest developments in geometric frameworks for complex biological structures such as spatial networks and functional data, longitudinal models of biological processes, the rise of geometric deep learning and AI in biomedicine and finally the importance of community-maintained open-source softwares and datasets.