Automatic Detection and Quantification of Ground-Glass Opacities on High-Resolution CT Using Multiple Neural Networks
- 1 November 2000
- journal article
- research article
- Published by American Roentgen Ray Society in American Journal of Roentgenology
- Vol. 175 (5), 1329-1334
- https://doi.org/10.2214/ajr.175.5.1751329
Abstract
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.Keywords
This publication has 21 references indexed in Scilit:
- Automatische Berechnung des Milzvolumens aus Spiral-CT-Daten mit Hilfe neuronaler Netze und „Fuzzy Logik”∗RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2000
- Computer-aided diagnosis for detection of interstitial opacities on chest radiographs.American Journal of Roentgenology, 1998
- Neural networks for the analysis of small pulmonary nodulesClinical Imaging, 1997
- Usual Interstitial PneumoniaInvestigative Radiology, 1997
- Method for segmenting chest CT image data using an anatomical model: preliminary resultsIEEE Transactions on Medical Imaging, 1997
- Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis.Radiology, 1995
- Differentiation between nodules and end-on vessels using a convolution neural network architectureJournal of Digital Imaging, 1995
- Detection of lung nodules in digital chest radiographs using artificial neural networks: A pilot studyJournal of Digital Imaging, 1995
- Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based schemeJournal of Digital Imaging, 1994
- Analysis of a simple self-organizing processBiological Cybernetics, 1982