Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images

TL;DR

This paper introduces SEAN, a novel neural network that leverages brain symmetry in CT images to improve the segmentation accuracy of acute ischemic infarcts, aiding better stroke diagnosis.

Contribution

The paper proposes a symmetry enhanced attention network that automatically aligns CT images to a standard space and integrates symmetry-aware attention for improved infarct segmentation.

Findings

SEAN outperforms existing symmetry-based methods in dice coefficient.

SEAN achieves better infarct localization accuracy.

The method effectively captures long-range dependencies across brain hemispheres.

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 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.