FA-UNet: A FasterNet and Attention-Gated Hybrid Network for Precise Ischemic Stroke Segmentation.

Abstract

Background: Accurate and timely segmentation of ischemic stroke lesions from diffusion-weighted imaging (DWI) is crucial for diagnosis and treatment planning. Manual segmentation is labor-intensive, time-consuming, and prone to inter-observer variability. This study aims to develop and validate a novel deep learning framework that overcomes the common trade-off between high segmentation accuracy and the computational efficiency required for practical clinical use.

Methods: We developed FasterNet and Attention-Gated UNet (FA-UNet), a hybrid U-Net-based architecture. The model's design features two key innovations: a computationally efficient FasterNet block at the bottleneck to capture global lesion context and multi-scale attention gates (MSAGs) on the skip connections to adaptively refine features and suppress noise. The model was trained and validated on the public Ischemic Stroke Lesion Segmentation (ISLES) 2022 dataset (n = 250 patients) and its performance was assessed on an independent, private test set of 600 DWI scans from 80 patients. FA-UNet's performance was benchmarked against several state-of-the-art U-Net variants using the Dice coefficient, Intersection over Union (IoU), sensitivity, and precision as primary outcome measures.

Results: On the independent test set (n = 80), the proposed FA-UNet model achieved a Dice coefficient of 0.8676 and an IoU of 0.7584. This performance surpassed all benchmarked architectures, including U-Net, U-Net3plus, and CMU-Net. Compared with the next best performing model, this represents a relative improvement of approximately 1.64% in the Dice score and 1.42% in IoU.

Conclusion: The FA-UNet architecture establishes a new state-of-the-art performance benchmark for automated ischemic stroke segmentation. By effectively balancing high accuracy with computational efficiency, it offers a robust, reliable, and clinically viable tool.