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Combination of computer-aided detection algorithms for automatic lung nodule identification

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.

Methods

The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings.

Results

The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches.

Conclusions

Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.

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Authors and Affiliations

  1. Dipartimento di Fisica dell’Università di Pisa, Pisa, Italy

    Niccolò Camarlinghi & Maria Evelina Fantacci

  2. Istituto Nazionale di Fisica Nucleare Sezione di Pisa, Pisa, Italy

    Niccolò Camarlinghi, Alessandra Retico & Maria Evelina Fantacci

  3. Bracco Imaging SpA, Milano, Italy

    Ilaria Gori

  4. Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari, Bari, Italy

    Roberto Bellotti, Gianfranco Gargano & Rosario Megna

  5. Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy

    Roberto Bellotti, Gianfranco Gargano & Rosario Megna

  6. Center of Innovative Technologies for Signal Detection and Processing (TIRES), Bari, Italy

    Roberto Bellotti

  7. Dipartimento di Fisica dell’Università di Genova, Genova, Italy

    Paolo Bosco

  8. Istituto Nazionale di Fisica Nucleare Sezione di Genova, Genova, Italy

    Paolo Bosco

  9. Istituto Nazionale di Fisica Nucleare Sezione di Torino, Torino, Italy

    Piergiorgio Cerello

  10. CEADEN, Ciudad Habana, Cuba

    Ernesto Lopez Torres

  11. Dipartimento di Fisica dell’Università del Salento, Lecce, Italy

    Marco Peccarisi

  12. Istituto Nazionale di Fisica Nucleare Sezione di Lecce, Lecce, Italy

    Marco Peccarisi

Authors
  1. Niccolò Camarlinghi
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  2. Ilaria Gori
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  3. Alessandra Retico
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  4. Roberto Bellotti
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  5. Paolo Bosco
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  6. Piergiorgio Cerello
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  7. Gianfranco Gargano
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  8. Ernesto Lopez Torres
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  9. Rosario Megna
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  10. Marco Peccarisi
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  11. Maria Evelina Fantacci
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Corresponding author

Correspondence to Niccolò Camarlinghi.

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Camarlinghi, N., Gori, I., Retico, A. et al. Combination of computer-aided detection algorithms for automatic lung nodule identification. Int J CARS 7, 455–464 (2012). https://doi.org/10.1007/s11548-011-0637-6

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  • DOI: https://doi.org/10.1007/s11548-011-0637-6

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