Abstract #4243

Toward automatic lesion transmurality assessment using machine learning: a proof of concept in preclinical EP studies under MRI-guidance

Valéry Ozenne^1,2,3,4, Pierre Bour^2,3,4, Marylène Delcey^2,3,4, Nicolas Cedilnik⁵, Maxime Sermesant⁵, and Bruno Quesson^2,3,4

¹Centre de Résonance Magnétique des Systèmes Biologiques, UMR 5536, CNRS, Bordeaux, France, ²IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France, ³Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France, ⁴INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France, ⁵Université Côte d’Azur, Inria, Epione, Sophia Antipolis, France

MR-guidance of electrophysiological (EP) procedures requires manual segmentation of the cardiac cavities either at the beginning of the procedure to produce the roadmap volume or after radiofrequency ablation (RFA) to assess the lesion transmurality in post ablation images. The purpose of this work is to evaluate the feasibility of automatic in-line segmentation in the context of routine preclinical EP studies.

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