| 产银萍,肖玲玲.电子密度模体插件自动定位方法[J].中国医学影像技术,2019,35(3):428~432 |
| 电子密度模体插件自动定位方法 |
| Method on automatic location of inserts in electron density phantom |
| 投稿时间：2018-07-15 修订日期：2018-11-21 |
| DOI：10.13929/j.1003-3289.201807113 |
| 中文关键词: 锥束计算机体层摄影术 深度卷积神经网络 电子密度模体 图像分割 |
| 英文关键词:cone-beam computed tomography deep convolution neural network electron density phantom image segmentation |
| 基金项目:国家重点研发计划（2016YFC0105102）。 |
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| 中文摘要: |
| 目的 探讨基于深度卷积神经网络（DCNN）对电子密度模体（CIRS 062）插件自动定位的方法。方法 首先基于DCNN模型分割CIRS 062的吸气态肺、呼气态肺、松质骨和密质骨4个插件；之后采用摩尔邻域追踪算法处理插件边缘；最后根据几何特征定位其他4个插件。结果 基于DCNN分割结果的戴斯相似性系数均>0.85，精确度均>0.81，综合评价指标均>0.61。结论 基于DCNN方法可实现插件自动定位。 |
| 英文摘要: |
| Objective To investigate automatic location of inserts in the electron density phantom (CIRS 062) based on deep neural network (DCNN). Methods Firstly, four inserts in CIRS 062 were segmented with DCNN model, namely the inhaled lung, the exhaled lung, the solid trabecular bone and the solid dense bone. Then Moore-neighbor tracking algorithm was used to process the segmentation results to obtain the precise segmentation edges. Finally, the other four inserts were located based on the geometric features. Results The results of Dice similarity coefficient were all >0.85, the precision were all>0.81, and F1-measure were all>0.61 based on DCNN. Conclusion The method based on DCNN can realize the automatic positioning of the inserts. |
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