Abstract #0788

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

Guang Yang^1,2, Jun Chen³, Zhifan Gao⁴, Shuo Li⁴, Hao Ni^5,6, Elsa Angelini⁷, Tom Wong^1,2, Raad Mohiaddin^1,2, Eva Nyktari², Ricardo Wage², Lei Xu⁸, Yanping Zhang³, Xiuquan Du³, Heye Zhang⁹, David Firmin^1,2, and Jennifer Keegan^1,2

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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