This paper describes a system for automatic detection of pulmonary nodules in lung CT (Computed Tomography) images. After modelling the activity of a single radiologist as two subsequent phases, namely, the regions of interest (ROIs) detection phase and the nodule detection phase, we built a system which emulates a team of radiologists. This is achieved by providing a further phase of collaboration and opinion exchange among the experts at the end of each of the previous phases. We also present experimental results, based on the ROC convex hull method, which show how the team of radiologists obtains better performance than the single best radiologist in both phases. In particular, we achieved a sensitivity of 92.48% against a specificity of about 83.54% in the nodule detection phase.
Modelling a Team of Radiologists for Lung Nodule Detection in CT Scans
ANTONELLI, MICHELA;COCOCCIONI, MARCO;FROSINI, GRAZIANO;LAZZERINI, BEATRICE;MARCELLONI, FRANCESCO
2007-01-01
Abstract
This paper describes a system for automatic detection of pulmonary nodules in lung CT (Computed Tomography) images. After modelling the activity of a single radiologist as two subsequent phases, namely, the regions of interest (ROIs) detection phase and the nodule detection phase, we built a system which emulates a team of radiologists. This is achieved by providing a further phase of collaboration and opinion exchange among the experts at the end of each of the previous phases. We also present experimental results, based on the ROC convex hull method, which show how the team of radiologists obtains better performance than the single best radiologist in both phases. In particular, we achieved a sensitivity of 92.48% against a specificity of about 83.54% in the nodule detection phase.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
