Vascular networks, from low-level vision to generative models


Hugues Talbot (CVN, CentraleSupélec, INRIA, Université Paris-Saclay)
November 27, 2019 — 11:00 — Location: Salle des séminaires du L2S

Abstract

The study of vascular networks is important in medical imaging because disease affecting blood vessels is the first cause of mortality and morbidity in the Western world. Yet, surprisingly, theses studies have not been the subject of major research efforts. From the low-level vision point of view, the vast majority of image processing techniques assume objects that are locally isotropic, whereas blood vessels are always thin and oriented at every scale. They are also inherently 3D and cannot usually be studied correctly in projections. With respect to scale, most blood vessels are too thin to be imaged irrespective of the imaging modality. Yet various blood vessel diseases, affecting blood perfusion for example, occur in vessels that cannot be imaged in MRI or scanner. In this talk, I will outline research performed in the last few years in this area. I will present some efficient low-level vision filters designed for thin and elongated objects. I will also show some recent work using a generative model (not based on deep learning) to produce realistic patient-specific vessel models that can be used to produce a forward imaging model for perfusion. This model can be used to solve related inverse problems, such as finding the cause of a perfusion deficit from observed perfusion.

Biography

Hugues Talbot received the engineering degree from Ecole Centrale de Paris (now CentraleSupelec) in 1989; the DEA (Master’s degree) from Université Paris VI (now Université Pierre et Marie Curie) in 1990; the PhD from Ecole des Mines de Paris (now Mines Paristech) and MIT in 1993 and the Habilitation from Université Paris Est (soon to be called Université Gustave Eiffel) on Friday the 13th 2013. Put off by the state of flux of French higher education, he left for Australia in 1994 and in due time became a principal research scientist at CSIRO, in mathematics and statistics department. Slowly realizing that things were actually not much better there, he came back in 2004 to take a professorship position at ESIEE Paris. He eventually became Dean for Research there, before joining CentraleSupelec as a professor in 2018, in the computer vision department (CVN). His interests include but are not limited to computer vision, medical imaging, image restoration, optimisation, machine learning, mathematical morphology and discrete geometry.