基于Mask R-CNN的宫颈细胞图像分割  

Cervical cell image segmentation based on Mask R-CNN

作　　者：郑杨 梁光明[2] 刘任任[1] Zheng Yang;Liang Guangming;Liu Renren(School of Computer Science,Xiangtan University,Xiangtan,Hunan 411100,China;School of Electronic Science and Engineering,National University of Defense Technology)

机构地区：[1]湘潭大学计算机学院·网络空间安全学院,湖南湘潭411105 [2]国防科技大学电子科学与工程学院

出　　处：《计算机时代》2020年第10期68-72,共5页Computer Era

摘　　要：宫颈细胞图像中目标分割的精度直接影响对疾病的判别和诊断,宫颈细胞图像中有不同种类的多个目标,所以有必要对宫颈细胞图像进行实例分割。为了获得更好的宫颈细胞图像实例分割效果,文章在Mask R-CNN的基础上提出了一种宫颈细胞图像实例分割方法,在网络中的特征金字塔网络(FPN)中加入空洞卷积将其改造为DFPN,减少图像信息的损失来提升分割的准确度。在TCTCOCO数据集的测试结果表明,该方法提高了宫颈细胞图像分割的精度。The accuracy of object segmentation in cervical cell image directly affects the identification and diagnosis of the disease.There are many different kinds of objects in cervical cell image,so it is necessary to segment cervical cell image by instance.In order to obtain a better instance segmentation effect of cervical cell image,this paper proposes an instance segmentation method of cervical cell image based on Mask R-CNN.The feature pyramid network(FPN)in the network is added with dilated convolution to transform it into DFPN to reduce the loss of image information and improve the accuracy of segmentation.The results of test on TCTCOCO data set show that the method improves the accuracy of cervical cell image segmentation.

关 键 词：卷积神经网络 宫颈细胞显微图像 实例分割 空洞卷积 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]