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Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation

  • Jun Chen
  • Guang Yang
  • Zhifan Gao
  • Hao Ni
  • Elsa Angelini
  • Raad Mohiaddin
  • Tom Wong
  • Yanping ZhangEmail author
  • Xiuquan DuEmail author
  • Heye Zhang
  • Jennifer Keegan
  • David Firmin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jun Chen
    • 1
  • Guang Yang
    • 2
    • 3
  • Zhifan Gao
    • 4
  • Hao Ni
    • 5
    • 6
  • Elsa Angelini
    • 7
  • Raad Mohiaddin
    • 2
    • 3
  • Tom Wong
    • 2
    • 3
  • Yanping Zhang
    • 1
    Email author
  • Xiuquan Du
    • 1
    Email author
  • Heye Zhang
    • 4
  • Jennifer Keegan
    • 2
    • 3
  • David Firmin
    • 2
    • 3
  1. 1.Anhui UniversityHefeiChina
  2. 2.Cardiovascular Research Centre, Royal Brompton HospitalLondonUK
  3. 3.National Heart & Lung InstituteImperial College LondonLondonUK
  4. 4.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  5. 5.Department of MathematicsUniversity College LondonLondonUK
  6. 6.Alan Turing InstituteLondonUK
  7. 7.Faculty of Medicine, Department of Surgery & CancerImperial College LondonLondonUK

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