Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).
Reference: Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.
Penso M, Babbaro M, Moccia S, Guglielmo M, Carerj ML, Giacari CM, Chiesa M, Maragna R, Rabbat MG, Barison A, Martini N, Pepi M, Caiani EG, Pontone G. J Cardiovasc Magn Reson. 2022 Nov 28;24(1):62.