17th of June - Francisco (Kiko) Belchí (La Salle – Universitat Ramon Llull)
Title: Topological Data Analysis of Lung Structure: A Persistent Homology Approach to Chronic Obstructive Pulmonary Disease (COPD)
Abstract: Persistent Homology is a tool from Topological Data Analysis (TDA) that captures the multiscale shape and connectivity structure of data. By examining how topological features (such as connected components, loops, and cavities) persist across scales, it provides a quantitative description of complex geometric patterns that traditional methods often miss. In this talk, I will introduce the core ideas of topology and Persistent Homology with intuitive examples. I will then show how these methods can be applied to medical imaging, focusing on my work on COPD, where Persistent Homology captures structural features that distinguish healthy lungs from those affected by COPD. This illustrates how topological features can become biomarkers in clinical applications.