Lisa CORBAT : "Fusion of complementary segmentations of medical images by Artificial Intelligence and other conflict management methods"

Travaux de thèse de Lisa Corbat :"Fusion of complementary segmentations of medical images by Artificial Intelligence and other conflict management methods"

The defense is public. The presentation will be in French, as well as the support. Given the health measures currently in force, the wearing of a mask (not provided) is mandatory for all persons wishing to attend.

Abstract : Nephroblastoma is the most common kidney tumour in children and its diagnosis is based exclusively on imaging. This work, representing the focus of our research, is part of a larger project: the European project SAIAD (Automated Segmentation of Medical Images Using Distributed Artificial Intelligence). The aim of the project is to design a platform capable of performing different automatic segmentations from source images using Artificial Intelligence (AI) methods, and thus obtain a faithful three-dimensional reconstruction. In this sense, work carried out in a previous thesis of the research team led to the creation of a segmentation platform. It allows the segmentation of several structures individually, by methods such as Deep Learning, and more particularly Convolutional Neural Networks (CNNs), as well as Case Based Reasoning (CBR). However, it is then necessary to automatically fuse the segmentations of these different structures in order to obtain a complete relevant segmentation. When aggregating these structures, contradictory pixels may appear. These conflicts can be resolved by various methods based or not on AI and are the subject of our research. First, we propose a fusion approach not focused on AI using the combination of six different methods, based on different imaging and segmentation criteria. In parallel, two other fusion methods are proposed using, a CNN coupled to the CBR for one, and a CNN using a specific existing segmentation learning method for the other. These different approaches were tested on a set of 14 nephroblastoma patients and demonstrated their effectiveness in resolving conflicting pixels and their ability to improve the resulting segmentations.

Jury composition : 

M. Jean-Philippe Thiran, Professeur des Universités, École Polytechnique Fédérale de Lausanne, Reporter ;

M. Daniel Racoceanu, Professeur des Universités, Paris Sorbonne Université, Reporter ;

M. Henri Souchay, Docteur-Ingénieur, Clinical Research Manager for France, Global Research Organization, GE Healthcare, Reviewer ;

M. Frédéric Auber, Professeur des Universités-praticien hospitalier, UBFC Centre Hospitalier Régional Universitaire de Besançon, Reviewer ;

M. Julien Henriet, Maître de Conférences, Université de Franche-Comté,PhD Codirector  ;

M. Jean-Christophe Lapayre, Professeur des Universités, Université de Franche-Comté, PhD Director.

Localization : Amphithéâtre J-J Gagnepain - bâtiment TEMIS, 15B Avenue des Montboucons - Besançon