{"title":"基于DenseNet-Attention-Unet模型的视网膜血管分割","authors":"Zhen Liang, Huazhu Liu, Xiaofang Zhao, Li Yu","doi":"10.1145/3411016.3411167","DOIUrl":null,"url":null,"abstract":"The feature information of retinal vascular image is complex. There are some problems in the existing algorithms, such as poor segmentation effect of microvascular segmentation and pathological vascular misclassification. Therefore, a vascular segmentation model based on DenseNet-Attention-unet (DA-Unet) is proposed. Firstly, retinal blood vessels were enhanced by adaptive histogram equalization and gamma correction, and then the DA-Unet model was constructed by DenseNet, Convolutional Block Attention Module and U-Net for retinal blood vessel segmentation.The experimental results show that the average accuracy of retinal vascular segmentation is 97.01%, and the ROC is 98.65%, when it was tested on the DRIVE data set.","PeriodicalId":251897,"journal":{"name":"Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research","volume":"91 7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Segmentation of Retinal Vessels Based on DenseNet-Attention-Unet Model Network\",\"authors\":\"Zhen Liang, Huazhu Liu, Xiaofang Zhao, Li Yu\",\"doi\":\"10.1145/3411016.3411167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The feature information of retinal vascular image is complex. There are some problems in the existing algorithms, such as poor segmentation effect of microvascular segmentation and pathological vascular misclassification. Therefore, a vascular segmentation model based on DenseNet-Attention-unet (DA-Unet) is proposed. Firstly, retinal blood vessels were enhanced by adaptive histogram equalization and gamma correction, and then the DA-Unet model was constructed by DenseNet, Convolutional Block Attention Module and U-Net for retinal blood vessel segmentation.The experimental results show that the average accuracy of retinal vascular segmentation is 97.01%, and the ROC is 98.65%, when it was tested on the DRIVE data set.\",\"PeriodicalId\":251897,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research\",\"volume\":\"91 7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3411016.3411167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3411016.3411167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Segmentation of Retinal Vessels Based on DenseNet-Attention-Unet Model Network
The feature information of retinal vascular image is complex. There are some problems in the existing algorithms, such as poor segmentation effect of microvascular segmentation and pathological vascular misclassification. Therefore, a vascular segmentation model based on DenseNet-Attention-unet (DA-Unet) is proposed. Firstly, retinal blood vessels were enhanced by adaptive histogram equalization and gamma correction, and then the DA-Unet model was constructed by DenseNet, Convolutional Block Attention Module and U-Net for retinal blood vessel segmentation.The experimental results show that the average accuracy of retinal vascular segmentation is 97.01%, and the ROC is 98.65%, when it was tested on the DRIVE data set.