Effect of CT image reconstruction methods on performance of pulmonary nodules detection algorithm based on deep learning
10.13929/j.1003-3289.201909048
- Author:
Zhenjuan LIU
1
Author Information
1. Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Jiangsu Province Academy of Traditional Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Deep learning;
Pulmonary nodules;
Reconstruction algorithm;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2019;35(12):1775-1779
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the impact of CT image reconstruction methods on the performance of pulmonary nodule detection algorithm based on deep learning (DL). Methods: Lung CT images of 298 cases were labeled by 2 attending doctors, and the inconsistent results between them were checked by a senior doctor. The final labels were regarded as the gold standards of this experiment. Pulmonary nodule detection algorithm was constructed based on a deep neural network and tested on these 298 cases. Comparing the output of the detection algorithm with the doctor's labeling, the sensitivity, accuracy and F1-score of the algorithm were calculated, especially those under different CT image reconstruction methods. Afterwards, the false-positive detections of the algorithm were checked, and the detailed distribution of these false positives was presented. Diagnostic effects of the model were analyzed among different CT image reconstruction algorithms. Results: The sensitivity of pulmonary nodule detection algorithm under mediastinum, lung, and bone CT reconstruction methods was 92.33% (313/339), 86.97% (287/330) and 92.73% (319/344), while the precision was 23.55% (313/1 329), 37.91% (287/757) and 27.84% (319/1 146), respectively. Taken sensitivity and precision into account, F1-socre of these 3 reconstruction methods was 0.38, 0.53 and 0.43, respectively (all P>0.05). Conclusion: Pulmonary nodule detection algorithm based on DL achieves excellent performance under pulmonary window reconstruction, mediastinum window reconstruction and bone window reconstruction, which can help doctors to improve work efficiency and diagnose quality.
