Method on automatic location of inserts in electron density phantom
10.13929/j.1003-3289.201807113
- Author:
Yinping CHAN
1
Author Information
1. School of Information Engineering, Jiangxi University of Science and Technology
- Publication Type:Journal Article
- Keywords:
Cone-beam computed tomography;
Deep convolution neural network;
Electron density phantom;
Image segmentation
- From:
Chinese Journal of Medical Imaging Technology
2019;35(3):428-432
- CountryChina
- Language:Chinese
-
Abstract:
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.
