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

arXiv: https://arxiv.org/abs/2110.05039 · HTML: https://mlsanity.com/paper/2110.05039/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation](https://mlsanity.com/paper/1911.10360/) (2019)
- [Segmenting Brain Tumors with Symmetry](https://mlsanity.com/paper/1711.06636/) (2017)

## Built on by (most cited first, 17 of 17)

- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans](https://mlsanity.com/paper/2206.15445/) (2022)
- [APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge](https://mlsanity.com/paper/2309.15243/) (2023)
- [Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training](https://mlsanity.com/paper/2201.01426/) (2022)
- [Dynamically Masked Discriminator for Generative Adversarial Networks](https://mlsanity.com/paper/2306.07716/) (2023)
- [Benchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CT](https://mlsanity.com/paper/2509.23132/) (2025)
- [Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement](https://mlsanity.com/paper/2203.03622/) (2022)
- [Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast Computed Tomography Images for Enhanced Treatment and Prognosis](https://mlsanity.com/paper/2411.09402/) (2024)
- [Synchronous Image-Label Diffusion Probability Model with Application to Stroke Lesion Segmentation on Non-contrast CT](https://mlsanity.com/paper/2307.01740/) (2023)
- [CPAISD: Core-penumbra acute ischemic stroke dataset](https://mlsanity.com/paper/2404.02518/) (2024)
- [Leveraging Unlabeled Scans for NCCT Image Segmentation in Early Stroke Diagnosis: A Semi-Supervised GAN Approach](https://mlsanity.com/paper/2511.19576/) (2025)
- [Clinically-aligned ischemic stroke segmentation and ASPECTS scoring on NCCT imaging using a slice-gated loss on foundation representations](https://mlsanity.com/paper/2602.23961/) (2026)
- [Image-Domain Poisson-Perturbation Robustness of NCCT Slice Classification](https://mlsanity.com/paper/2603.26764/) (2026)
- [DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation](https://mlsanity.com/paper/2606.18886/) (2026)
- [FSB-Net: Frequency-Spatial Boundary Network for Brain Stroke Lesion Segmentation in Non-Contrast CT](https://mlsanity.com/paper/2607.20955/) (2026)
- [AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages](https://mlsanity.com/paper/2608.19769/) (2026)
- [Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation](https://mlsanity.com/paper/2609.20151/) (2026)

## Abstract (arXiv)

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.