Lung nodule detection in low-dose and thin-slice computed tomography

Authors:
A. Retico;P. Delogu;M. E. Fantacci;I. Gori;A. Preite Martinez
Affiliations:
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy and Dipartimento di Fisica dell'Universití di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy and Dipartimento di Fisica dell'Universití di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy and Bracco Imaging S.p.A., Via E. Folli 50, 20134 Milano, Italy;Centro Studi e Ricerche Enrico Fermi, Via Panisperna 89/A, 00184 Roma, Italy
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

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).