This Research Chair drives medical image analysis toward greater precision by combining geometry, computer vision, and artificial intelligence. Its objective: to leverage anatomical geometry to better understand the human body and its disorders. The research program aims to overcome current limitations in medical imaging, particularly imprecision of morphological measurements and inconsistent image quality. To address these challenges, the team innovates through statistical modeling of anatomical shapes, learning models under geometric constraints, and shape-based medical prediction. By specifically targeting bone, brain, and cardiac structures, this research enables concrete advances, such as optimization of knee arthroplasty or the analysis of cardiac fibers. Ultimately, these new algorithms assist in diagnosis, surgery, and follow-up, thereby directly improving quality of life for patients.