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用于烟雾病检测的Faster RCNN改进算法 被引量:2

Improved Faster RCNN Algorithm for Moyamoya Disease Detection
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摘要 为了预防烟雾病引发的并发症威胁患者生命,需要对烟雾病进行及时有效的诊断。本文提出了一种改进的Faster RCNN算法用于烟雾病检测。首先,提取颈内动脉数字减影血管造影(Digital subtraction angiography,DSA)图像,并进行数据增强,训练集、验证集和测试集之比为6∶2∶2。使用ResNet101网络作为特征提取网络,避免血管特征在卷积和池化过程中产生模糊或丢失;结合区域生成网络(Region proposal network,RPN),定位烟雾病病灶的位置;再将Faster RCNN模型中的ROI Pooling替换为ROI Align进行特征映射,避免由量化带来的误差影响。本文采用平均精度(Average precision,AP)作为算法检测性能的评估指标,所用方法对正常样本和烟雾病样本检测的AP分别为99.23%和89.39%。实验结果表明,该方法可以实现烟雾病的快速有效检测,可在复杂的血管网中准确检测烟雾病病灶的位置,为烟雾病辅助诊断提供一定的技术支持。 To prevent complication caused by moyamoya disease from threatening patients’lives,timely and effective diagnosis of moyamoya disease is needed.An improved Faster RCNN algorithm for moyamoya disease detection is presented.Firstly,the digital subtraction angiography(DSA)image of internal carotid artery is extracted and enhanced.The ratio of training set,verification set and test set is 6∶2∶2.ResNet101 network is used as the feature extraction network to avoid blurring or loss of vascular features in the process of convolution and pooling.Combined with region proposal network(RPN),the location of moyamoya disease focus is located.Then replace ROI pooling in Faster RCNN model with ROI Align for feature mapping to avoid the error impact caused by quantization.The average precision(AP)is used as the evaluation index of the detection performance of the algorithm.The AP of normal samples and moyamoya disease samples are 99.23%and 89.39%,respectively.Experimental results show that the proposed method can realize the rapid and effective detection of moyamoya disease.It can accurately detect the location of moyamoya disease lesions in the complex vascular network,and provide some technical support for the auxiliary diagnosis of moyamoya disease.
作者 徐佳薇 武杰 雷宇 顾宇翔 XU Jiawei;WU Jie;LEI Yu;GU Yuxiang(School of Health Science&Engineering,University of Shanghai for Science&Technology,Shanghai 200093,China;Department of Neurosurgery,Huashan Hospital,Fudan University,Shanghai 200040,China)
出处 《数据采集与处理》 CSCD 北大核心 2022年第6期1391-1400,共10页 Journal of Data Acquisition and Processing
基金 国家自然科学基金(61605114)。
关键词 烟雾病检测 数字减影血管造影 深度学习 FasterRCNN 特征提取 detection of moyamoya disease digital subtraction angiography(DSA) deep learning Faster RCNN feature extraction
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