Related Experiment Video
Updated: Aug 29, 2025
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
Published on: June 20, 2025
Differentiation and prediction of pneumoconiosis stage by computed tomography texture analysis based on U-Net neural
Xinxin Hu1, Rongsheng Zhou2, Maoneng Hu2
1School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
Background And Objective:
The progressive worsening of pneumoconiosis will ensue a hazardous physical condition in patients. This study details the differential diagnosis of the pneumoconiosis stage, by employing computed tomography (CT) texture analysis, based on U-Net neural network.
Methods:
The pneumoconiosis location from 92 patients at various stages was extracted by U-Net neural network. Mazda software was employed to analyze the texture features. Three dimensionality reduction methods set the best texture parameters. We applied four methods of the B11 module to analyze the selected texture parameters and calculate the misclassified rate (MCR). Finally, the receiver operating characteristic curve (ROC) of the texture parameters was analyzed, and the texture parameters with diagnostic efficiency were evaluated by calculating the area under curve (AUC).
Results:
The original film was processed by Gaussian and Laplace filters for a better display of the segmented area of pneumoconiosis in all stages. The MCR value obtained by the NDA analysis method under the MI dimension reduction method was the lowest, at 10.87%. In the filtered texture feature parameters, the best AUC was 0.821.
Conclusions:
CT texture analysis based on the U-Net neural network can be used to identify the staging of pneumoconiosis.
