Jiarui Xing1, Sona Ghadimi2, Mohammad Abdi2, Kenneth C Bilchick3, Frederick H Epstein2, and Miaomiao Zhang1
1Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 2Department of Biomedical Engineering, University of VIrginia, Charlottesville, VA, United States, 3School of Medicine, University of Virginia, Charlottesville, VA, United States
1Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 2Department of Biomedical Engineering, University of VIrginia, Charlottesville, VA, United States, 3School of Medicine, University of Virginia, Charlottesville, VA, United States
This work introduces an end-to-end multi-task deep learning network for fully automatic cardiac activation time detection, which offers: (i) prediction of late-activated regions and activation time (ii) fast & accurate 3D activation map (iii) no labor-intensive hand tuning process
Fig 2. Illustration of the proposed method, including the multi-task network aiming joint regression and classification.
Fig 4. A comparison of 3D activation map reconstructed by (a) manual label; (b) active contour; (c) regression network; and (d) our method.