基于FDMU-net的早产儿视网膜图像血管分割

Retinal Vessel Segmentation of Prematurity Infants Based on FDMU-net

早产儿视网膜病变是一种血管增生性疾病,是导致新生儿视网膜受损或致盲的主要原因之一。通过分割和分析早产儿眼底图像中的血管结构,可以对早产儿视网膜病变进行早期诊断和监测。早产儿视网膜血管较成年人视网膜血管对比度低且存在脉络膜重叠等问题,从而会导致视网膜血管分割准确率不高和敏感度较低等问题。为解决这些问题,在U-net框架下提出FDMU-net新生儿视网膜血管分割模型。该模型融入密集连接层来提高特征利用率,在编码和解码通道拼接过程中,采用多尺度卷积核特征融合方式来提高感受野,并利用血管骨架加权focal loss损失函数来提高网络对模糊样本的分割精度。利用本文提出的FDMU-net模型在DRIVE和STARE两个公开数据集上进行实验,准确率分别达到96.75%和96.85%,敏感度分别达到81.52%和84.84%。在临床早产儿眼底数据集实验中,对比U-net模型、AttentionResU-net模型及多尺度特征融合全卷积神经网络模型,本文提出的FDMU-net模型在准确率和敏感度上有较大的提高,可较好地解决血管丢失及敏感度较低的问题,有效分割出早产儿视网膜血管。

Retinopathy of prematurity infants is an angiogenic disease.It is one of the main causes of neonatal retina damage or blindness.By segmenting and analyzing the structure of fundus vessels,early diagnosis and monitoring of retinopathy of prematurity infants can be performed.Compared with adult retinal vessels,those of prematurity infants have lower contrast and choroid overlap,which leads to low accuracy and sensitivity of retinal vessel segmentation.Therefore,the FDMU-net neonatal retinal segmentation model is proposed under the U-net framework.The model incorporates dense connection layers to improve feature utilization.During encoding and decoding channel stitching,multi-scale convolution kernels are used to fuse features,which improve the receptive field.Finally,the weighted focal loss of the vessel skeleton is used as a loss function to improve the network’s problem of poor segmentation of fuzzy samples.The algorithm proposed in this paper was tested on two public datasets,DRIVE and STARE,with an accuracy of 96.75%and 96.85%,and sensitivity of 81.52%and 84.84%,respectively.Moreover,after performing experiments on the fundus dataset of prematurity infants,compared with the U-net model,the AttentionResU-Net model and the multi-scale feature fusion full convolutional neural network model,the proposed FDMU-net model has higher accuracy and sensitivity.In conclusion,the algorithm proposed in this paper can satisfactorily solve the problems of vessel loss and low sensitivity in vessel segmentation and effectively segment the retinal vessel of prematurity infants.