A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study

Alessandra Retico; Francesco Bagagli; Niccolo Camarlinghi; Carmela Carpentieri; Maria Evelina Fantacci; Ilaria Gori

doi:10.1117/12.811721

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3 March 2009 A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study

Alessandra Retico, Francesco Bagagli, Niccolo Camarlinghi, Carmela Carpentieri, Maria Evelina Fantacci, Ilaria Gori

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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601S (2009) https://doi.org/10.1117/12.811721
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

The computer-aided detection (CAD) system we applied on the ANODE09 dataset is devoted to identify pulmonary nodules in low-dose and thin-slice computed tomography (CT) images: we developed two different systems for internal (CAD_I) and juxtapleural nodules (CAD_JP) in the framework of the italian MAGIC-5 collaboration. The basic modules of CADI subsystem are: a 3D dot-enhancement algorithm for nodule candidate identification and an original approach, we referred as Voxel-Based Neural Approach (VBNA), to reduce the amount of false-positive findings based on a neural classifier working at the voxel level. To detect juxtapleural nodules we developed the CADJP subsystem based on a procedure enhancing regions where many pleura surface normals intersect, followed by a VBNA classification. We present both the FROC curves we obtained on the 5 annotated ANODE09 example dataset, and on all the ANODE09 50 test cases.

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Alessandra Retico, Francesco Bagagli, Niccolo Camarlinghi, Carmela Carpentieri, Maria Evelina Fantacci, and Ilaria Gori "A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601S (3 March 2009); https://doi.org/10.1117/12.811721

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