融合轮廓信息与条件生成对抗的视网膜血管分割

Segmentation of retinal vessels by fusing contour information and conditional generative adversarial

针对现有视网膜血管分割算法存在主血管末端易断裂、中心黄斑和视盘边界易误分割等问题,本文提出一种融合血管轮廓信息与条件生成对抗网络的视网膜血管分割算法。首先,采用非均匀光照移除和主成分分析处理眼底图像,增强血管与背景的对比度,并获得特征信息丰富的单尺度灰度图像。其次,将集成了带偏移量的深度可分离卷积和挤压激励(SE)模块的密集块同时运用到编码器和解码器,缓解梯度消失和梯度爆炸,同时使得网络专注于学习目标的特征信息。然后,引入轮廓损失函数,提升网络对血管信息和轮廓信息的辨识能力。最后,在DRIVE与STARE数据集上分别进行实验,受试者曲线值分别达到0.9825和0.9874,准确率分别达到0.9677和0.9756。实验结果表明,本文提出的算法能够准确辨别轮廓与血管,减少血管断裂,在临床眼科疾病诊断中具有一定的应用价值。

The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break,and the central macula and the optic disc boundary are likely to be mistakenly segmented.To solve the above problems,a novel retinal vessels segmentation algorithm is proposed in this paper.The algorithm merged together vessels contour information and conditional generative adversarial nets.Firstly,non-uniform light removal and principal component analysis were used to process the fundus images.Therefore,it enhanced the contrast between the blood vessels and the background,and obtained the single-scale gray images with rich feature information.Secondly,the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception(SE)block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion.Simultaneously,the network focused on the feature information of the learning target.Thirdly,the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network.Finally,experiments were carried out on the DRIVE and STARE datasets respectively.The value of area under the receiver operating characteristic reached 0.9825 and 0.9874,respectively,and the accuracy reached 0.9677 and 0.9756,respectively.Experimental results show that the algorithm can accurately distinguish contours and blood vessels,and reduce blood vessel rupture.The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.