基于改进Res-UNet的视网膜图像血管分割  

Improved Res-UNet-based vascular segmentation of retinal images

作　　者：杨涵 李柏承[1] 陈玲玲 YANG Han;LI Baicheng;CHEN Lingling(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《光学仪器》2023年第4期24-31,共8页Optical Instruments

基　　金：国家自然科学基金(62005165)。

摘　　要：精准的视网膜血管分割可以辅助诊疗如糖尿病、高血压等疾病。眼睛血管结构和病理特征的复杂性导致血管分割的精度和速度都存在很多局限。为了克服这一问题,提出了一种改进的U-net分割方法,该方法将U-net网络解码器和编码器中的卷积模块改为残差模块,使用非局部注意模块连接编码器和解码器。网络模型在不增加参数量的情况下,通过添加残差模块和注意力机制提高了像素之间的信息相关性以及模型提取特征的能力。最后,采用DRIVE数据集对所提模型与原U-net网络进行对比评价,新模型在测试集上的特征检测准确率、特异性、灵敏度和Dice系数分别达到了0.9679、0.9896、0.8245和0.8281。实验结果证明,所提网络模型可对视网膜进行精确地血管分割。Accurate retinal vascular segmentation supports the treatment of diseases such as diabetes and hypertension.Because of the complex vascular structure of the eye,the complexity of the pathological features leads to many limitations in the accuracy and speed of vascular segmentation.To overcome this problem,an improved U-net segmentation method is proposed,which replaces the convolution module in the U-net network decoder and encoder with a residual module,using a non-local attention module to connect the encoder and decoder.The network model enhances the correlation of pixel information and the ability to extract features without increasing the number of parameters.Finally,the DRIVE dataset was used for comparison and evaluation with the original U-net network,and the model achieved 0.9679、0.9896、0.8245 and 0.8281 of feature detection accuracy,specificity,sensitivity and Dice coefficient on the test set,respectively.The experimental results demonstrate that the proposed network model can perform accurate vascular segmentation of the retina.

关 键 词：图像分割 视网膜图像 残差模块 卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]