Multiview two-task recursive attention model for left atrium and atrial scars segmentation

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Title: Multiview two-task recursive attention model for left atrium and atrial scars segmentation
Authors: Chen, J
Yang, G
Gao, Z
Ni, H
Angelini, E
Mohiaddin, R
Wong, T
Zhang, Y
Du, X
Zhang, H
Keegan, J
Firmin, D
Item Type: Conference Paper
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.
Issue Date: 26-Sep-2018
Date of Acceptance: 1-Sep-2018
URI: http://hdl.handle.net/10044/1/71711
DOI: https://doi.org/10.1007/978-3-030-00934-2_51
ISBN: 9783030009335
ISSN: 0302-9743
Publisher: Springer
Start Page: 455
End Page: 463
Journal / Book Title: Lecture Notes in Computer Science (LNCS) proceedings
Copyright Statement: © Springer Nature Switzerland AG 2018. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-00934-2_51
Conference Name: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Keywords: cs.CV
cs.CV
eess.IV
08 Information and Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Conference Place: Granada, Spain
Online Publication Date: 2018-09-26
Appears in Collections:Department of Surgery and Cancer