School of Medical Information, Wannan Medical College, Wuhu 241002, China.
Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China.
Math Biosci Eng. 2022 Jul 12;19(10):9948-9965. doi: 10.3934/mbe.2022464.
In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.
在眼科领域，视网膜疾病常常伴有并发症，而有效地分割视网膜血管是判断视网膜疾病的重要条件。因此，本文提出了一种用于视网膜血管分割的分割模型。生成对抗网络（GAN）已被用于图像语义分割，并表现出良好的性能。因此，本文提出了一种改进的 GAN。在 R2U-Net 的基础上，生成器添加了注意力机制、通道和空间注意力，这可以减少信息的损失并提取更有效的特征。我们在判别器中使用密集连接模块。密集连接模块具有缓解梯度消失和实现特征重用的特点。经过一定数量的迭代训练后，可以区分生成的预测图和标签图。基于传统 GAN 中的损失函数，我们引入了均方误差。通过使用这个损失，我们确保合成图像包含更真实的血管结构。所提出方法在三个公共数据集 DRIVE、CHASE-DB1 和 STARE 的视网膜血管像素分割中的曲线下面积（AUC）值分别为 0.9869、0.9894 和 0.9885。与之前的方法相比，该实验的指标有所提高。
