ANR-19-CE45-0001-01
Porteur : Stéphanie Bricq
Abstract :
Late gadolinium enhancement (LGE) imaging has been widely used for detection and assessment of myocardial scar and presence of fibrosis in cardiac magnetic resonance imaging (MRI). LGE is a gold standard for the quantification of focal myocardial fibrosis, but in some cardiomyopathies the fibrotic process is often diffuse. To overcome this problem, T1 mapping techniques have been developed to quantify diffuse myocardial fibrosis and to characterize tissues.
The aim of this project is to combine automatically information from LGE and T1 mapping images by developing a Bayesian deep learning method to automatically detect area of fibrosis and to automatically classify the different pathologies and identify normal cases.
Consortium :
– Stéphanie Bricq (ImViA)
– Fabrice Meriaudeau (ImViA)
– Alain Lalande (ImViA)
– Alexandre Cochet (ImViA)
– Thibault Leclercq (CHU Dijon)
– Alexis Jacquier (AP-HM Marseille)
Début du projet : 01/01/2020
Fin du projet : 31/12/2023
Job offers
– PhD : Bayesian Deep Learning for cardiac LGE and T1 mapping images segmentation for cardiac LGE and T1 mapping images segmentation and pathology classification : Position closed
– Ingénieur d’études
