光学学报, 2018, 38 (11): 1111004, 网络出版: 2019-05-09
基于改进卷积神经网络的视网膜血管图像分割 下载: 1706次
Retinal Vessel Image Segmentation Based on Improved Convolutional Neural Network
图像处理 图像分割 视网膜血管 卷积神经网络 深度学习 image processing image segmentation retinal vessels convolutional neural network deep learning
摘要
彩色眼底图像视网膜血管分割对于临床医学诊断有重要价值。提出了一种基于改进卷积神经网络的视网膜血管分割方法。首先,将残差学习和密集连接网络(DenseNet)相结合,更充分地利用每一层的特征;通过增加短连接的方式,缩短了低层特征图到高层特征图之间的路径,强化了特征的传播能力。其次,为了提取更多细小血管,在编码器-解码器结构的网络中加入了空洞卷积,在不增加参数的情况下增加感受野。实验结果表明,与现存其他深度学习方法相比,所提出网络结构的参数数量更少,在DRIVE标准数据集上平均准确率达到0.9556,灵敏度达到0.8036,特异性达到0.9778,受试者工作特性(ROC)曲线下的面积(AUC)达到0.9800,比现存其他深度学习方法的分割效果更优。
Abstract
The retinal vessel segmentation in color fundus images is of great value for the clinical diagnosis and a retinal vessel segmentation method based on an improved convolutional neural network is proposed. First, the residual learning is combined with the densely connected network (DenseNet) to fully exploit the feature maps of each layer. The path from the low-level feature maps to the high-level ones via the addition of shortcuts is shortened and the feature propagation ability is strengthened. Second, as for the extraction of more fine vessels, the dilated convolutions are adopted in the encoder-decoder network to expand the receptive field without the increase of parameters. The experimental results show that the proposed network structure has less parameters, compared with the other existing deep learning methods. The average accuracy on the DRIVE datasets is up to 0.9556, the sensitivity is up to 0.8036, the specificity is up to 0.9778, the area under curve of receiver operating characteristic reaches 0.9800, better than the segmentation effects of the other existing deep learning methods.
吴晨玥, 易本顺, 章云港, 黄松, 冯雨. 基于改进卷积神经网络的视网膜血管图像分割[J]. 光学学报, 2018, 38(11): 1111004. Chenyue Wu, Benshun Yi, Yungang Zhang, Song Huang, Yu Feng. Retinal Vessel Image Segmentation Based on Improved Convolutional Neural Network[J]. Acta Optica Sinica, 2018, 38(11): 1111004.