Abstract
Quantitative estimation of the acute ischemic infarct is crucial to improve neurological outcomes of the patients with stroke symptoms. Since the density of lesions is subtle and can be confounded by normal physiologic changes, anatomical asymmetry provides useful information to differentiate the ischemic and healthy brain tissue. In this paper, we propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The transformed image is further processed by a U-shape network integrated with the proposed symmetry enhanced attention for pixel-wise labelling. The symmetry enhanced attention can efficiently capture context information from the opposite side of the image by estimating long-range dependencies. Experimental results show that the proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
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Acknowledgments
This work was supported in part by following grants, MOST-2018AAA0102004, NSFC-62061136001, the Key Program of Bei**g Municipal Natural Science Foundation (7191003), and the Key Projects of the National Natural Science Foundation of China (81830057).
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Liang, K. et al. (2021). Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-contrast CT Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_41
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DOI: https://doi.org/10.1007/978-3-030-87234-2_41
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