Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images
This work addresses the challenge of accurate infarct segmentation for stroke patients, which is crucial for improving neurological outcomes, but it is incremental as it builds on existing symmetry-based methods.
The paper tackled the problem of segmenting acute ischemic infarcts from non-contrast CT images by proposing a symmetry-enhanced attention network (SEAN) that leverages anatomical asymmetry, resulting in improved performance over state-of-the-art methods in dice coefficient and infarct localization.
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.