MRAU-net:基于强化特征提取的视网膜血管分割
MRAU-net: Retinal vessel segmentation based on enhanced feature extraction
投稿时间:2022-10-12  修订日期:2023-02-08
DOI:
中文关键词:  图像处理  视网膜血管分割  Unet  全卷积神经网络  多尺度特征提取  多通道注意力机制
英文关键词:image processing  fundus vessel segmentation  Unet  full convolutional neural network  multiscale residual convolution  multichannel attention module
基金项目:国家重大项目“工业领域知识自动构建与推理决策技术及应用”,上海市信息安全综合管理技术研究重点实验室开放研究课题基金资助
作者单位邮编
孙国栋 上海工程技术大学 201620
闫丰亭* 上海工程技术大学 201620
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中文摘要:
      视网膜血管的分割精确率对眼科疾病和糖尿病早期诊断有着重要影响。面对现有方法在微血管与病变区域分割性能差的问题,本文提出一种最大化提取血管信息的分割模型MRAU-net。该模型在编码部位引入多尺度特征提取残差模块和多级残差空洞卷积层,用来扩展感受野,学习多层次图像特征,提高模型对血管信息的利用率;下采样和短连接部位分别融入CBAM注意力机制和多通道注意力模块,增加模型对血管的识别度,降低误分割的可能性。本文基于DRIVE和STARE两种公开数据集进行了实验,来验证改进模型的分割能力。结论表明, 两种数据上的准确率(Acc)分别为0.9652和0.9715,灵敏度(Se)分别为0.8205和0.8256,与其它算法相比分割性能更有优势。
英文摘要:
      The segmentation accuracy of retinal vessels has an important impact on the early diagnosis of ophthalmic diseases and diabetes. Facing the problem of poor segmentation performance of existing methods in microvessel and lesion regions, the paper proposes a segmentation model MRAU-net that maximizes the extraction of vascular information. The model introduces a multiscale feature extraction residual module and a multilevel residual null convolution layer at the encoding site, which is used to extend the perceptual field, learn multilevel image features and improve the utilization of vascular information by the model; the downsampling and short connection sites are incorporated into the CBAM attention mechanism and multi-channel attention module are incorporated to increase the recognition of blood vessels by the model and reduce the possibility of mis-segmentation. In this paper, experiments are conducted based on two publicly available datasets, DRIVE and STARE, to verify the segmentation capability of the improved model. The conclusions show that the accuracy (Acc) is 0.9652 and 0.9715 on both data, and the sensitivity (Se) is 0.8205 and 0.8256, respectively, which are more advantageous than other algorithms in terms of segmentation performance.
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