Abstract #4189

Automated myocardial scar segmentation on joint bright- and black-blood late gadolinium enhancement images

Thaïs Génisson¹, Victor de Villedon de Naide^1,2,3, Kalvin Narceau¹, Baptiste Durand^1,2, Jean-David Maes², Manuel Villegas-Martinez^1,2, Théo Richard¹, Kun He¹, Pauline Gut^1,4, Pierre Jaïs^1,5, Matthias Stuber^1,4,6, Hubert Cochet^1,2, and Aurélien Bustin^1,2,4

¹IHU LIRYC, Heart rhythm disease institute, Université de Bordeaux – INSERM U1045, Bordeaux, France, ²Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Bordeaux, France, ³Center for Medical Physics and Biomedical Engineering, High Field MR Center, Medical University of Vienna, Vienna, Austria, ⁴Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁵Department of Cardiac Electrophysiology, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Bordeaux, France, ⁶CIBM Center for Biomedical Imaging, Lausanne, Switzerland

Synopsis

Keywords: Myocardium, Segmentation

Motivation: Myocardial scar assessment is crucial for accurate patient prognostic. Magnetic resonance bright-blood sequences are used to retrieve heart anatomy information, while black-blood technique has shown promise for enhanced scar detection. However, manual delineation of infarction is time-consuming, operator-dependent and labor-intensive, limiting its accuracy and increasing its variability.

Goal(s): To develop a fast, accurate and automated method for improved scar segmentation and analysis on co-registered bright- and black-blood images.

Approach: Leverage an artificial intelligence-based pipeline to achieve automated scar segmentation.

Results: Automated scar segmentation was achieved in an accurate and fast fashion. Good agreement with conventional manual segmentation methods were found.

Impact: The proposed artificial intelligence-based pipeline exploits anatomical information and improved scar visualization from joint bright- and black-blood images. This permits fast, operator-independent, and time-saving scar segmentation and analysis for the radiologist, with required clinical quality to better help guide therapy.

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