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RESEARCH PRODUCT

Automated detection of lung nodules in low-dose computed tomography

D. CascioS. C. CheranA. ChincariniG. De NunzioP. DeloguM. E. FantacciG. GarganoI. GoriG. L. MasalaA. Preite MartinezA. ReticoM. SantoroC. SpinelliT. Tarantino

subject

Computer-aided detectionLow-dose computed tomography (LDCT)Computer-aided detection (CAD)thin slice CTLung cancer screeninglung cancer screeningFOS: Physical sciencesComputer-aided detection (CAD); Low-dose computed tomography (LDCT); Lung cancer screening; Thin-slice CTMedical Physics (physics.med-ph)Thin-slice CTlow-dose computed tomographyPhysics - Medical Physicsimage processing

description

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scan

yearjournalcountryeditionlanguage
2007-07-18
10.1007/s11548-007-0107-3http://hdl.handle.net/11568/116600