Lung nodule detection in low-dose and thin-slice computed tomography.
A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).
Retico
A
A
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy. retico@df.unipi.it
Delogu
P
P
Fantacci
M E
ME
Gori
I
I
Preite Martinez
A
A
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D007558:Italy; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008403:Mass Screening; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011829:Radiation Dosage; D012680:Sensitivity and Specificity; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D036542:Tomography, Spiral Computed