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., Delogu, P., Fantacci, M.E., Gori, I., Martinez, A.P. (2008). Lung nodule detection in low-dose and thin-slice computed tomography. COMPUTERS IN BIOLOGY AND MEDICINE, 38(4), 525-534 [10.1016/j.compbiomed.2008.02.001].

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

Delogu, P.;
2008-01-01

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).
2008
Retico, A., Delogu, P., Fantacci, M.E., Gori, I., Martinez, A.P. (2008). Lung nodule detection in low-dose and thin-slice computed tomography. COMPUTERS IN BIOLOGY AND MEDICINE, 38(4), 525-534 [10.1016/j.compbiomed.2008.02.001].
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0010482508000206-main.pdf

non disponibili

Tipologia: PDF editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 755.43 kB
Formato Adobe PDF
755.43 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1006313