  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 Ushape 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.
