基于卷积神经网络的翼状胬肉病灶分割研究  

Research on segmentation of pterygium lesions based on convolutional neural networks

作　　者：朱绍军 方新闻 郑博 吴茂念 杨卫华 Shao-Jun Zhu;Xin-Wen Fang;Bo Zheng;Mao-Nian Wu;Wei-Hua Yang(School of Information Engineering,Huzhou University,Huzhou 313000,Zhejiang Province,China;Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources,Huzhou University,Huzhou 313000,Zhejiang Province,China;Big Data Laboratory of Ophthalmic Artificial Intelligence,the Affiliated Eye Hospital of Nanjing Medical University,Nanjing 210029,Jiangsu Province,China)

机构地区：[1]湖州师范学院信息工程学院,中国浙江省湖州市313000 [2]浙江省现代农业资源智慧管理与应用研究重点实验室,中国浙江省湖州市313000 [3]南京医科大学附属眼科医院眼科人工智能大数据实验室,中国江苏省南京市210029

出　　处：《国际眼科杂志》2022年第6期1016-1019,共4页International Eye Science

基　　金：国家自然科学青年基金项目(No.61906066);浙江省自然科学基金项目(No.LQ18F020002);南京市企业专家工作室(团队)项目;湖州师范学院2022年校级研究生科研创新项目(No.2022KYCX38)。

摘　　要：目的:通过深度卷积神经网络方法对翼状胬肉病灶进行精准分割。方法:在PSPNet模型结构的基础上构建Phase-fusion PSPNet网络结构用于翼状胬肉病灶的分割,该网络在金字塔池化模块后接入阶段上采样模块,以分阶段增大为原则逐步上采样,减少信息丢失,适合于边缘模糊的分割任务。将南京医科大学附属眼科医院提供的翼状胬肉眼表图像517张分为训练集(330张)、验证集(37张)、测试集(150张),其中训练集和验证集图像用于训练,测试集图像仅用于测试。比较翼状胬肉病灶智能分割和专家标注的结果。结果:构建Phase-fusion PSPNet网络结构针对测试数据集的翼状胬肉病灶分割单类平均交并比(MIOU)和平均像素精确度(MPA)分别为86.31%和91.91%;翼状胬肉单类交并比(IOU)和像素精确度(PA)分别为77.64%和86.10%。结论:卷积神经网络可以实现翼状胬肉病灶的精准分割,有助于为医生进行进一步疾病诊断和手术建议提供重要参考,同时实现翼状胬肉智能诊断的可视化。AIM:To study the precise segmentation of pterygium lesions using the convolutional neural networks from artificial intelligence.METHODS:The network structure of Phase-fusion PSPNet for the segmentation of pterygium lesions is proposed based on the PSPNet model structure.In our network,the up-sampling module is connected behind the pyramid pooling module,which gradually increase the sampling based on the principle of phased increase.Therefore,the information loss is reduced,it is suitable for segmentation tasks with fuzzy edges.The experiments conducted on the dataset provided by the Affiliated Eye Hospital of Nanjing Medical University,which includes 517 ocular surface photographic images of pterygium were divided into training set(330 images),validation set(37 images)and test set(150 images),which the training set and the validation set images are used for training,and the test set images are only used for testing.Comparing results of intelligent segmentation and expert annotation of pterygium lesions.RESULTS:Phase-fusion PSPNet network structure for pterygium mean intersection over union(MIOU)and mean average precision(MPA)were 86.31%and 91.91%,respectively,and pterygium intersection over union(IOU)and average precision(PA)were 77.64%and 86.10%,respectively.CONCLUSION:Convolutional neural networks can segment pterygium lesions with high precision,which is helpful to provide an important reference for doctors’further diagnosis of disease and surgical recommendations,and can also visualize the pterygium intelligent diagnosis.

关 键 词：翼状胬肉 图像分割 深度学习 卷积神经网络 PSPNet 

分 类 号：R777.33[医药卫生—眼科]