Automated detection and quantification of micronodules in thoracic CT scans to identify subjects at risk for silicosis

Silica dust-exposed individuals are at high risk of developing silicosis, a fatal and incurable lung disease. The presence of disseminated micronodules on thoracic CT is the radiological hallmark of silicosis but locating micronodules, to identify subjects at risk, is tedious for human observers. We present a computer-aided detection scheme to automatically find micronodules and quantify micronodule load. The system used lung segmentation, template matching, and a supervised classification scheme. The system achieved a promising sensitivity of 84% at an average of 8.4 false positive marks per scan. In an independent data set of 54 CT scans in which we defined four risk categories, the CAD system automatically classified 83% of subjects correctly, and obtained a weighted kappa of 0.76.