Radiol Artif Intell 2021 Jan 25;3(1):e200021. Epub 2020 Nov 25.
Departments of Radiology (A.B., A.J.) and Cardiology (G.H.), Hôpital de la Timone Adultes, AP-HM, 264, rue Saint-Pierre 13385 Marseille Cedex 05, France; CRMBM-UMR CNRS 7339, Medical Faculty, Aix-Marseille University, Marseille, France (A.B., J.F., Z.B., M.B., A.J.); I2M-UMR CNRS 7373, Aix-Marseille University, Centrale Marseille, Marseille, France (J.F., B.G.); ImVia Laboratory and University Hospital of Dijon, Bourgogne-Franche Comté University, Dijon, France (A.L.); Department of Radiology, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France (L.B.); Department of Cardiovascular Imaging, Lille University Hospital, Lille, France (F.P.); and Department of Diagnostic Imaging, Rouen University Hospital, Rouen, France (J.N.D.).
Purpose: To develop and evaluate a complete deep learning pipeline that allows fully automated end-diastolic left ventricle (LV) cardiac MRI segmentation, including trabeculations and automatic quality control of the predicted segmentation.
Materials And Methods: This multicenter retrospective study includes training, validation, and testing datasets of 272, 27, and 150 cardiac MR images, respectively, collected between 2012 and 2018. The reference standard was the manual segmentation of four LV anatomic structures performed on end-diastolic short-axis cine cardiac MRI: LV trabeculations, LV myocardium, LV papillary muscles, and the LV blood cavity. Read More