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Abstract #0788

A Deep Learning Based Left Atrium Anatomy Segmentation and Scar Delineation in 3D Late Gadolinium Enhanced CMR Images

Guang Yang1,2, Jun Chen3, Zhifan Gao4, Shuo Li4, Hao Ni5,6, Elsa Angelini7, Tom Wong1,2, Raad Mohiaddin1,2, Eva Nyktari2, Ricardo Wage2, Lei Xu8, Yanping Zhang3, Xiuquan Du3, Heye Zhang9, David Firmin1,2, and Jennifer Keegan1,2

1National Heart and Lung Institute, Imperial College London, London, United Kingdom, 2Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, 3Anhui University, Anhui, China, 4Department of Medical Imaging, Western University, London, ON, Canada, 5Department of Mathematics, University College London, London, United Kingdom, 6Alan Turing Institute, London, United Kingdom, 7NIHR Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom, 8Department of Radiology, Beijing Anzhen Hospital, Beijing, China, 9School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China

3D late gadolinium enhanced (LGE) CMR images of left atrial (LA) scar tissue can be used to stratify patients with atrial fibrillation and to guide subsequent ablation therapy. This requires a segmentation of the LA anatomy (usually from an anatomical acquisition) and a further segmentation of the scar tissue within the LA (from a 3D LGE acquisition). We propose a deep learning based framework incorporating multiview information and attention mechanism to solve both LA anatomy and scar segmentations simultaneously from a single 3D LGE acquisition. Compared to existing methods, we show improved segmentation accuracy (mean Dice=93%/87% for LA/scar).

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