用改进的注意力U-Net精准分割新冠肺部感染区域
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans
- 在U-Net中引入注意力机制提升细节捕捉能力
- Dice系数达0.8658，平均IoU为0.8316
- 适合医学影像分析与临床辅助诊断研究者
本研究提出一种基于改进U-Net架构的卷积神经网络方法，用于自动分割新冠患者CT图像中的感染肺部区域。该方法融合注意力机制、数据增强和后处理技术，在公开数据集上取得Dice系数0.8658和平均IoU 0.8316的性能，优于现有方法。数据来自公开资源并经过增强以提升多样性。结果表明该模型具有优越的分割能力。未来工作包括扩展数据集规模、探索3D分割以及推进临床部署。
原文摘要 · Abstract (English)
In this study, we propose a robust methodology for automatic segmentation of infected lung regions in COVID-19 CT scans using convolutional neural networks. The approach is based on a modified U-Net architecture enhanced with attention mechanisms, data augmentation, and postprocessing techniques. It achieved a Dice coefficient of 0.8658 and mean IoU of 0.8316, outperforming other methods. The dataset was sourced from public repositories and augmented for diversity. Results demonstrate superior segmentation performance. Future work includes expanding the dataset, exploring 3D segmentation, and preparing the model for clinical deployment.
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