改进的UNet模型提升心脏影像分割精度，尤其在心室与瘢痕组织识别上表现优异。
DADU: Dual Attention-based Deep Supervised UNet for Automated Semantic Segmentation of Cardiac Images
- 融合通道与空间注意力及边缘感知跳跃连接，增强特征表达
- 达98%的骰子相似系数，显著降低豪斯多夫距离
- 适合临床心脏病灶自动分析，对医学影像分割有实用价值
我们提出一种基于深度学习的心脏磁共振（CMR）图像分割模型，用于自动分割左心室、右心室及心肌瘢痕组织。该方法结合UNet架构、通道与空间注意力机制、基于边缘检测的跳跃连接以及深度监督学习，以提升分割准确性。通过多通道处理生成多个特征图，构建双注意力机制融合通道与空间信息；利用提取的边缘信息优化跳跃连接中的特征重建；深度监督有效缓解深层神经网络分类中的梯度消失问题。实验结果表明，该方法在Dice相似系数（DSC）上达到98%，豪斯多夫距离（HD）显著降低，优于当前主流技术。
原文摘要 · Abstract (English)
We propose an enhanced deep learning-based model for image segmentation of the left and right ventricles and myocardium scar tissue from cardiac magnetic resonance (CMR) images. The proposed technique integrates UNet, channel and spatial attention, edge-detection based skip-connection and deep supervised learning to improve the accuracy of the CMR image-segmentation. Images are processed using multiple channels to generate multiple feature-maps. We built a dual attention-based model to integrate channel and spatial attention. The use of extracted edges in skip connection improves the reconstructed images from feature-maps. The use of deep supervision reduces vanishing gradient problems inherent in classification based on deep neural networks. The algorithms for dual attention-based model, corresponding implementation and performance results are described. The performance results show that this approach has attained high accuracy: 98% Dice Similarity Score (DSC) and significantly lower Hausdorff Distance (HD). The performance results outperform other leading techniques both in DSC and HD.
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