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


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%).


CONCLUSIONS
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.
Kauczor
H U
HU
Department of Radiology, Johannes Gutenberg-University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.
Heitmann
K
K
Heussel
C P
CP
Marwede
D
D
Uthmann
T
T
Thelen
M
M
D015502:Absorptiometry, Photon; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011014:Pneumonia / Q000000981:diagnostic imaging; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011658:Pulmonary Fibrosis / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*