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

  • Niccolò Camarlinghi
  • Ilaria Gori
  • Alessandra Retico
  • Roberto Bellotti
  • Paolo Bosco
  • Piergiorgio Cerello
  • Gianfranco Gargano
  • Ernesto Lopez Torres
  • Rosario Megna
  • Marco Peccarisi
  • Maria Evelina Fantacci
Original Article

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.

Keywords

Computer-aided detection Computed tomography Lung cancer Medical image analysis Pattern recognition 

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References

  1. 1.
    Online document. American Cancer Society (2009) http://www.cancer.org/Research/CancerFactsFigures. Accessed 16 December 2010
  2. 2.
    Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM (2010) Estimates of worldwide burden of cancer in 2008. Int J Cancer 117(12): 2893–2917CrossRefGoogle Scholar
  3. 3.
    Diederich S, Lentschig MG, Overbeck TR, Wormanns D, Heindel W (2001) Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting. European Radiology 11(8): 1345–1350PubMedCrossRefGoogle Scholar
  4. 4.
    Investigators The International Early Lung Cancer Action Program: (2006) Survival of patients with stage I lung cancer detected on CT screening. New England J Med 355(17): 1763–1771CrossRefGoogle Scholar
  5. 5.
    Online document U.S. National Cancer Institute November (2010) http://www.cancer.gov/clinicaltrials/noteworthy-trials/nlst. Accessed 16 December 2010
  6. 6.
    Roberts HC, Patsios D, Kucharczyk DM, Paul N, Roberts TP (2005) The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT, Computer Assisted Radiology and Surgery, Proceedings of the 19th International Congress and Exhibition, Berlin, June 22–25, 2005, International Congress Series 1281, pp 1137–1142Google Scholar
  7. 7.
    Das M et al (2006) Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology 241: 564–571PubMedCrossRefGoogle Scholar
  8. 8.
    Brochu B et al (2007) Computer-aided detection of lung nodules on thin collimation MDCT: impact on radiologists’ performance. Journal de Radiologie 88(4): 573–578PubMedCrossRefGoogle Scholar
  9. 9.
    Matsumoto S et al (2008) Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings. Radiat Med 26(9): 562–569PubMedCrossRefGoogle Scholar
  10. 10.
    van Ginneken B, Armato SG 3rd, de Hoop B, Amelsvoort-van Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, Camarlinghi N, Bagagli F, Gori I, Hara T, Fujita H, Gargano G, Bellotti R, Tangaro S, Bolaños L, De Carlo F, Cerello P, Cristian Cheran S, Lopez Torres E, Prokop M (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6): 707–722PubMedCrossRefGoogle Scholar
  11. 11.
    Niemeijer M, Loog M, Abràmoff MD, Viergever MA, Prokop M, van Ginneken B (2011) On combining computer-aided detection systems. IEEE Trans Med Imaging 30(2): 215–223PubMedCrossRefGoogle Scholar
  12. 12.
    Bellotti R et al (2007) Distributed medical images analysis on a GRID infrastructure. Future Gener Comput Syst 23: 475–484CrossRefGoogle Scholar
  13. 13.
    Golosio B et al (2009) A novel multithreshold method for nodule detection in lung CT. Med Phys 36(8): 3607–3618PubMedCrossRefGoogle Scholar
  14. 14.
    De Nunzio G et al (2009) Automatic lung segmentation in CT images with accurate handling of the Hilar region. J Digit Imaging, Online FirstGoogle Scholar
  15. 15.
    Cerello P, Cheran SC, Bagagli F, Bagnasco S, Bellotti R, Bolanos L, Catanzariti E, De Nunzio G, Fiorina E, Gargano G, Gemme G, Lopez Torres E, Masala G, Peroni C and Santoro M (2008) The Channeler Ant Model: object segmentation with virtual ant colonies. In: IEEE nuclear science symposium conference records, 2008, 3147–3152Google Scholar
  16. 16.
    Cerello P et al (2010) 3-D object segmentation using ant colonies. Pattern Recognit 43(4): 1476–1490CrossRefGoogle Scholar
  17. 17.
    Fantacci ME et al Computer aided detection of nodules in low dose and thin slice lung CT. doi:10.1594/ecr2010/C-1053
  18. 18.
    Bellotti R et al (2007) A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Med Phys 34(12): 4901–4910PubMedCrossRefGoogle Scholar
  19. 19.
    Retico A, Delogu P, Fantacci ME, Gori I, Preite Martinez A (2008) Lung nodule detection in low-dose and thin-slice computed tomography. Comput Biol Med 38(4): 525–534PubMedCrossRefGoogle Scholar
  20. 20.
    Gori I, Bagagli F, Fantacci ME, Preite Martinez A, Retico A, De Mitri I, Donadio S, Fulcheri C, Gargano G, Magro R, Santoro M, Stumbo S (2007) Multi-scale analysis of lung computed tomography images. J Instrum 2(09):P09007Google Scholar
  21. 21.
    Retico A, Fantacci ME, Gori I, Kasae P, Golosio B, Piccioli A, Cerello P, De Nunzio G, Tangaro S (2009) Pleural nodule identification il low-dose and thin-slice lung computed tomography. Comput Biol Med 39(12): 1137–1144PubMedCrossRefGoogle Scholar
  22. 22.
    Retico A et al (2009) A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study. Proc SPIE, vol. 7260, 72601S-72601S-8Google Scholar
  23. 23.
    McNitt-Gray MF, Armato SG 3rd, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJ, MacMahon H, Kazerooni EA, Croft BY, Clarke LP (2007) The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol 14(12): 1464–1474PubMedCrossRefGoogle Scholar
  24. 24.
    https://wiki.nci.nih.gov/display/cip/lidc. Accessed 16 december 2010
  25. 25.
    Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, New YorkGoogle Scholar
  26. 26.
    Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B 36(1): 111–147Google Scholar
  27. 27.
    Retico A, Bagagli F, Camarlinghi N, Carpentieri C, Fantacci ME, Gori I (2009) A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study. In: Medical Imaging 2009: Computer-Aided Diagnosis, 7260, 72601S–8, SPIE, Lake Buena Vista, FL, USAGoogle Scholar
  28. 28.
    Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30(8): 2040–2051PubMedCrossRefGoogle Scholar
  29. 29.
    Paik SD, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB Jr, Yee J, Dey J, Napel S (2004) Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans Med Imaging 23(6): 661–675PubMedCrossRefGoogle Scholar
  30. 30.
    http://www.osirix-viewer.com/. Accessed 16 december 2010

Copyright information

© CARS 2011

Authors and Affiliations

  • Niccolò Camarlinghi
    • 1
    • 2
  • Ilaria Gori
    • 3
  • Alessandra Retico
    • 2
  • Roberto Bellotti
    • 4
    • 5
    • 6
  • Paolo Bosco
    • 7
    • 8
  • Piergiorgio Cerello
    • 9
  • Gianfranco Gargano
    • 4
    • 5
  • Ernesto Lopez Torres
    • 10
  • Rosario Megna
    • 4
    • 5
  • Marco Peccarisi
    • 11
    • 12
  • Maria Evelina Fantacci
    • 1
    • 2
  1. 1.Dipartimento di Fisica dell’Università di PisaPisaItaly
  2. 2.Istituto Nazionale di Fisica Nucleare Sezione di PisaPisaItaly
  3. 3.Bracco Imaging SpAMilanoItaly
  4. 4.Dipartimento Interateneo di Fisica “M. Merlin”Università degli Studi di BariBariItaly
  5. 5.Istituto Nazionale di Fisica Nucleare Sezione di BariBariItaly
  6. 6.Center of Innovative Technologies for Signal Detection and Processing (TIRES)BariItaly
  7. 7.Dipartimento di Fisica dell’Università di GenovaGenovaItaly
  8. 8.Istituto Nazionale di Fisica Nucleare Sezione di GenovaGenovaItaly
  9. 9.Istituto Nazionale di Fisica Nucleare Sezione di TorinoTorinoItaly
  10. 10.CEADENCiudad HabanaCuba
  11. 11.Dipartimento di Fisica dell’Università del SalentoLecceItaly
  12. 12.Istituto Nazionale di Fisica Nucleare Sezione di LecceLecceItaly

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