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.

翻译：本研究提出一种基于卷积神经网络的鲁棒性方法，用于在COVID-19 CT影像中自动分割感染肺区域。该方法采用改进的U-Net架构，通过注意力机制、数据增强及后处理技术进行增强。在公开数据集上经数据扩增后，模型取得了0.8658的Dice系数与0.8316的平均交并比，性能优于现有方法。实验结果表明该方法具有卓越的分割性能。未来工作将包括扩展数据集规模、探索三维分割方案以及推进模型的临床部署准备。