| 侍新,谢世朋,李海波.基于卷积神经网络检测肺结节[J].中国医学影像技术,2018,34(6):934~939 |
| 基于卷积神经网络检测肺结节 |
| Detection of pulmonary nodules based on conventional neural networks |
| 投稿时间：2017-12-04 修订日期：2018-04-09 |
| DOI：10.13929/j.1003-3289.201712025 |
| 中文关键词: 体层摄影术,X线计算机 图像分割 特征提取 卷积神经网络 位置敏感特征图 肺结节 |
| 英文关键词:Tomography, X-ray computed Image segmentation Feature extraction Convolution neural network Position-sensitive score maps Lung nodules |
| 基金项目:国家自然科学基金（11547155）、教育部-中国移动科研基金项目（MCM20150504）、江苏省高校自然科学基金（17KJB510038）、江苏省科技重点研发计划-产业前瞻与共性关键技术项目（BE2016001-4）、南京邮电大学科研基金项目（NY214026、NY217035）。 |
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| 英文摘要: |
| Objective Major challenges in the current automatic detection of lung nodules from chest CT images are to improve the sensitivity and to reduce the false positive rate. A new scheme based on convolutional neural network was proposed in this study. Methods The method applied an automatic anatomy recognition (AAR) methodology based on fuzzy modeling ideas and an iterative relative fuzzy connectedness (IRFC) delineation algorithm for the segmentation of lung parenchyma in CT images. The segmented lung image was inputted into the conventional neural networks for feature extraction of pulmonary nodules. The network adopted position-sensitive score maps to express the location information of lung nodules. Results This method could obtain accurate segmentation of the lung parenchyma in the data set of Tianchi Medical AI Contest, and the accuracy, sensitivity, specificity and false-positive rate of lung nodules detected was 95.60%, 95.24%, 95.97% and 4.03%, respectively. Conclusion Detection of pulmonary nodules based on convolutional neural networks has high accuracy and efficiency, and good robustness. |
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