  Lung cancer is the deadliest type of cancer worldwide and late detection is
the major factor for the low survival rate of patients. Low dose computed
tomography has been suggested as a potential screening tool but manual
screening is costly, time-consuming and prone to variability. This has fueled
the development of automatic methods for the detection, segmentation and
characterisation of pulmonary nodules but its application to clinical routine
is challenging. In this study, a new database for the development and testing
of pulmonary nodule computer-aided strategies is presented which intends to
complement current databases by giving additional focus to radiologist
variability and local clinical reality. State-of-the-art nodule detection,
segmentation and characterization methods are tested and compared to manual
annotations as well as collaborative strategies combining multiple radiologists
and radiologists and computer-aided systems. It is shown that state-of-the-art
methodologies can determine a patient's follow-up recommendation as accurately
as a radiologist, though the nodule detection method used shows decreased
performance in this database.
