Thesis: Probabilistic models for image processing: applications in vascular surgery

PhD defense: Hugo GANGLOFF

Title: Probabilistic models for image processing: applications in vascular surgery

Team: IMAGeS

Abstract: The work presented in this thesis deals with medical image segmentation in the field of vascular surgery, where like in most of the fields of medical research, the images deeply transform the medical knowledge and the treatments. The spread of endovascular surgical procedures and biomaterial implantations is permitted by the recent progress in image processing. However, many issues (corrupted and scarce images, huge data to process, ...) are still barriers to the development of safer and patient-specific surgical treatments. We address these issues via the study of new probabilistic models, notably via hidden Markov models. The new models are thus motivated by real issues. However, they offer significant methodological contributions for statistical image processing and have been developed in a rigorous theoretical context. In particular, new models of hidden Markov chain and pairwise hidden Markov field are proposed, in order to model highly correlated noises. A triplet Markov tree model is developed to enhance the spatial correlations in the classical Markov tree model, based on properties of Markov fields. Inference and parameter estimation within the new models are specifically treated in our studies. The models are first tested on synthetic data and compared to other alternative approaches from the literature. Eventually, the models are applied to real images from vascular surgery available at the Geprovas laboratory.

The jury is composed by:

• Isabelle Bloch, Professeur, Télécom Paris, Rapportrice
• Nabil Chakfé, Professeur, Université de Strasbourg, Co-directeur de thèse
• Christophe Collet, Professeur, Université de Strasbourg, Directeur de thèse
• Mauro Gargiulo, Professeur, Université de Bologne, Membre invité
• Emmanuel Monfrini, Maître de Conférences, Télécom SudParis, Encadrant de thèse
• Wojciech Pieczynski, Professeur, Télécom SudParis, Examinateur
• Cédric Richard, Professeur, Université Nice Sophia Antipolis, Rapporteur

The defense will take place on December, 15 2020 at 2 p.m.

Keywords: probabilistic graphical models, hidden Markov models, statistical image segmentation, medical imaging