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.

The purpose of this study was to implement neural networks and expert rules for the automatic detection of ground glass opacities (GG) on high-resolution computed tomography (HRCT). Different approaches using self-organizing neural nets as well as classifications of lung HRCT with and without the use of explicit textural parameters have been applied in preliminary studies. In the present study a hybrid network of three single nets and an expert rule was applied for the detection of GG on 120 HRCT scans from 20 patients suffering from different lung diseases. Single nets alone were not capable to reliably detect or exclude GG since the false-positive rate was greater than 100 % with regard to the area truly involved, more than 50 pixels throughout, and the true-positive rate was greater than 95 %. The hybrid network correctly classified 91 of 120 scans. Mild GG was false positive in 15 cases with less than 50 pixels, which was judged not clinically relevant. The pitfalls were: partial volume effects of bronchovascular bundles and the chest wall. Motion artefacts and diaphragm were responsible for 11 misclassifications. Hybrid networks represent a promising tool for an automatic pathology-detecting system. They are ready to use as a diagnostic assistant for detection, quantification and follow-up of ground glass opacities, and further applications are underway.

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Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans.