Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask

AJR Am J Roentgenol. 2000 Nov;175(5):1329-34. doi: 10.2214/ajr.175.5.1751329.


Objective: We compared multiple neural networks with a density mask for the automatic detection and quantification of ground-glass opacities on high-resolution CT under clinical conditions.

Subjects and methods: Eighty-four patients (54 men and 30 women; age range, 18-82 years; mean age, 49 years) with a total of 99 consecutive high-resolution CT scans were enrolled in the study. The neural network was designed to detect ground-glass opacities with high sensitivity and to omit air-tissue interfaces to increase specificity. The results of the neural network were compared with those of a density mask (thresholds, -750/-300 H), with a radiologist serving as the gold standard.

Results: The neural network classified 6% of the total lung area as ground-glass opacities. The density mask failed to detect 1.3%, and this percentage represented the increase in sensitivity that was achieved by the neural network. The density mask identified another 17.3% of the total lung area to be ground-glass opacities that were not detected by the neural network. This area represented the increase in specificity achieved by the neural network. Related to the extent of the ground-glass opacities as classified by the radiologist, the neural network (density mask) reached a sensitivity of 99% (89%), specificity of 83% (55%), positive predictive value of 78% (18%), negative predictive value of 99% (98%), and accuracy of 89% (58%).

Conclusion: Automatic segmentation and quantification of ground-glass opacities on high-resolution CT by a neural network are sufficiently accurate to be implemented for the preinterpretation of images in a clinical environment; it is superior to a double-threshold density mask.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absorptiometry, Photon
  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Female
  • Follow-Up Studies
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Lung / diagnostic imaging*
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Pneumonia / diagnostic imaging
  • Predictive Value of Tests
  • Prospective Studies
  • Pulmonary Fibrosis / diagnostic imaging
  • Radiographic Image Interpretation, Computer-Assisted*
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods*