Automated detection of lung nodules in low-dose computed tomography

D. Cascio; S. C. Cheran; A. Chincarini; G. De Nunzio; P. Delogu; M. E. Fantacci; G. Gargano; I. Gori; G. L. Masala; A. Preite Martinez; A. Retico; M. Santoro; C. Spinelli; T. Tarantino

arXiv · 0707.2696

Automated detection of lung nodules in low-dose computed tomography

D. Cascio·S. C. Cheran·A. Chincarini·G. De Nunzio·P. Delogu·M. E. Fantacci·G. Gargano·I. Gori·G. L. Masala·A. Preite Martinez·A. Retico·M. Santoro·C. Spinelli·T. Tarantino

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Abstract

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

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D. Cascio, S. C. Cheran, A. Chincarini, G. De Nunzio, P. Delogu, M. E. Fantacci, G. Gargano, I. Gori, G. L. Masala, A. Preite Martinez, A. Retico, M. Santoro, C. Spinelli, T. Tarantino. 2007-07-18. Automated detection of lung nodules in low-dose computed tomography. https://arxiv.org/abs/0707.2696

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