标题： LFRA-Net：一种轻量级焦点和区域感知注意力网络用于视网膜血管分割 显示英文标题

标题： LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio

摘要： 视网膜血管分割对于早期诊断威胁视力和系统性疾病的至关重要，尤其是在计算资源有限的实际临床环境中。尽管基于深度学习的分割方法取得了显著进步，但当前模型在提取微小血管方面仍面临挑战，并且计算成本较高。在本研究中，我们通过在编码器-解码器瓶颈处引入焦点调制注意力以及在选择性跳跃连接中引入区域感知注意力来提出LFRA-Net。LFRA-Net是一个轻量级网络，专为精确和有效的视网膜血管分割而优化。它通过高效捕捉局部和全局依赖关系来增强特征表示和区域关注。LFRA-Net在仅具有0.17百万参数、0.66 MB内存大小和10.50 GFLOPs的情况下，优于许多最先进的模型。我们在三个公开可用的数据集上验证了它：DRIVE、STARE和CHASE_DB。在DRIVE、STARE和CHASE_DB数据集上，其Dice分数（84.28%、88.44%和85.50%）和Jaccard指数（72.86%、79.31%和74.70%）表现更好。与现有的深度学习方法相比，LFRA-Net在分割精度和计算成本之间提供了理想的比率，这使其适用于资源有限地区的实时临床应用。代码可在https://github.com/Mehwish4593/LFRA-Net找到。 显示英文摘要

摘要： Retinal vessel segmentation is critical for the early diagnosis of vision-threatening and systemic diseases, especially in real-world clinical settings with limited computational resources. Although significant improvements have been made in deep learning-based segmentation methods, current models still face challenges in extracting tiny vessels and suffer from high computational costs. In this study, we present LFRA-Net by incorporating focal modulation attention at the encoder-decoder bottleneck and region-aware attention in the selective skip connections. LFRA-Net is a lightweight network optimized for precise and effective retinal vascular segmentation. It enhances feature representation and regional focus by efficiently capturing local and global dependencies. LFRA-Net outperformed many state-of-the-art models while maintaining lightweight characteristics with only 0.17 million parameters, 0.66 MB memory size, and 10.50 GFLOPs. We validated it on three publicly available datasets: DRIVE, STARE, and CHASE\_DB. It performed better in terms of Dice score (84.28\%, 88.44\%, and 85.50\%) and Jaccard index (72.86\%, 79.31\%, and 74.70\%) on the DRIVE, STARE, and CHASE\_DB datasets, respectively. LFRA-Net provides an ideal ratio between segmentation accuracy and computational cost compared to existing deep learning methods, which makes it suitable for real-time clinical applications in areas with limited resources. The code can be found at https://github.com/Mehwish4593/LFRA-Net.