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

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

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.

Citation Download Citation

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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