| 基于深度学习的CT影像肺结节检测 |
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| 引用本文： | 赵梓淇,裴昀,常振东,王博,安烁文,李文辉.基于深度学习的CT影像肺结节检测[J].吉林大学学报(信息科学版),2019,37(5):572-581. |
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| 作者姓名： | 赵梓淇 裴昀 常振东 王博 安烁文 李文辉 |
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| 作者单位： | 吉林大学计算机科学与技术学院,长春,130012;吉林大学电子科学与工程学院,长春,130012 |
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| 基金项目： | 吉林省自然科学基金资助项目( 20180101038JC) |
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| 摘 要： | 为辅助医生检测大量肺部CT( Computed Tomography) 图像中难以发现的肺结节的问题，提出使用深度U-Net网络对肺部结节进行分割。由于U-Net 网络出自于全卷积网络( FCN: Full Convolution Network) ，特点是端到端，像素到像素，网络对每个像素点进行分类，能将深层特征抽象的特征信息和浅层特征具象的位置信息相结合得到CT 图像的分割掩膜。实验结果表明，该方法对肺结节的检测效果良好。
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| 关 键 词： | 深度学习 肺结节 深度U-Net网络 语义分割 全卷积网络(FCN) |
Lung Nodules Detected by CT Image Based on Deep Learning |
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| ZHAO Ziqi,PEI Yun,CHANG Zhendong,WANG Bo,AN Shuowen,LI Wenhui.Lung Nodules Detected by CT Image Based on Deep Learning[J].Journal of Jilin University:Information Sci Ed,2019,37(5):572-581. |
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| Authors: | ZHAO Ziqi PEI Yun CHANG Zhendong WANG Bo AN Shuowen LI Wenhui |
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| Institution: | a． College of Computer Science and Technology; b． College of Electronic Science and Engineering，Jilin University，Changchun 130012，China |
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| Abstract: | To assist doctors in detecting large numbers of pulmonary nodules in lung CT ( Computed Tomography) images，lung nodules were segmented using the deep U-Net network． As U-Net network comes from FCN ( Full Convolution Network) ，it is characterized by end-to-end and pixel to pixel． The network classifies each pixel and can combine the abstract feature information of deep features with the position information of the concrete feature of shallow features to obtain the segmentation mask of CT image． Experimental results show that this method is effective in detecting lung nodules． |
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| Keywords: | deep learning lung nodules deep U-Net semantic segmentation full convolution network ( FCN) |
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