AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information |
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Institution: | 1. School of Data Science, Fudan University, Shanghai, China;2. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;3. School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK;4. Technical University Munich, Munich, Germany;5. Helmholtz Center Munich, Germany;1. Department of Electrical and Computer Engineering, University of California, Los Angeles, United States;2. Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada;3. Servier Virtual Cardiac Centre, Mazankowski Alberta Heart Institute, Edmonton, Canada;4. Department of Computing Science, University of Alberta, Edmonton, Canada;1. Wuhan National Laboratory for Optoelectronics, Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China;2. Department of Medical Biophysics, University of Toronto, Toronto, Canada;3. Sunnybrook Research Institute, University of Toronto, Toronto, Canada;1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China;2. Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China;3. Sustainable Computing Laboratory, University of Notre Dame, Notre Dame, USA |
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Abstract: | Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. The automatic segmentation is however still challenging due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an implicit surface projection to utilize the inherent correlation between LA cavity and scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 post-ablation LGE MRIs from the MICCAI2018 Atrial Segmentation Challenge. Moreover, we explored the domain generalization ability of the proposed AtrialJSQnet on 40 pre-ablation LGE MRIs from this challenge and 30 post-ablation multi-center LGE MRIs from another challenge (ISBI2012 Left Atrium Fibrosis and Scar Segmentation Challenge). Extensive experiments on public datasets demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code has been released via https://zmiclab.github.io/projects.html. |
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