Skip to main content
SpringerLink
Log in

Computer-aided detection and automated CT volumetry of pulmonary nodules

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

With use of multislice computed tomography (MSCT), small pulmonary nodules are being detected in vast numbers, constituting the majority of all noncalcified lung nodules. Although the prevalence of lung cancers among such lesions in lung cancer screening populations is low, their isolation may contribute to increased patient survival. Computer-aided diagnosis (CAD) has emerged as a diverse set of diagnostic tools to handle the large number of images in MSCT datasets and most importantly, includes automated detection and volumetry of pulmonary nodules. Current CAD systems can significantly enhance experienced radiologists’ performance and outweigh human limitations in identifying small lesions and manually measuring their diameters, augment observer consistency in the interpretation of such examinations and may thus help to detect significantly higher rates of early malignomas and give more precise estimates on chemotherapy response than can radiologists alone. In this review, we give an overview of current CAD in lung nodule detection and volumetry and discuss their relative merits and limitations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Marten K, Seyfarth T, Auer F et al (2004) Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists. Eur Radiol 14:1930–1938

    Article  PubMed  Google Scholar 

  2. Marten K, Grillhösl A, Seyfarth T, Obenauer S, Rummeny EJ, Engelke C (2005) Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings. Eur Radiol 15(2):203–212

    Article  PubMed  Google Scholar 

  3. Marten K, Engelke C, Seyfarth T, Grillhösl A, Obenauer S, Rummeny EJ (2005) Computer-aided detection of pulmonary nodu`les: influence of nodule characteristics on detection performance. Clin Radiol 60:196–206

    Article  PubMed  CAS  Google Scholar 

  4. Armato III SG, Li F, Giger ML, MacMahon H, Sone S, Doi K (2002) Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 225:685–692

    Article  PubMed  Google Scholar 

  5. Armato SG III, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: Preliminary results. Med Phys 28:1552–1561

    Article  PubMed  Google Scholar 

  6. Masutani Y, Macmahon H, Doi K (2002) Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans Med Imaging 21:1517–1523

    Article  PubMed  Google Scholar 

  7. Uppaluri R, Hoffman EA, Sonka M, Hunninghake GW, McLennan G (1999) Interstitial lung disease: a quantitative sudy using the adaptive multiple feature method. Am J Respir Crit Care Med 159:519–525

    PubMed  CAS  Google Scholar 

  8. Ko JP, Naidich DP (2004) Computer-aided diagnosis and the evaluation of lung disease. J Thorac Imaging 19:136–155

    Article  PubMed  Google Scholar 

  9. Wormanns D, Diederich S (2004) Characterization of small pulmonary nodules by CT. Eur Radiol 14:1380–1391

    Google Scholar 

  10. Marten K, Funke M, Engelke C (2004) Flat-panel detector-based volumetric CT: prototype evaluation with volumetry of small artificial nodules in a pulmonary phantom. J Thorac Imaging 19:156–163

    Article  PubMed  Google Scholar 

  11. Marten K, Engelke C, Grabbe E, Rummeny EJ (2004) Flat-panel detector-based computed tomography: accuracy of experimental growth rate assessment in pulmonary nodules. Fortschr Röntgenstr 176:752–757

    Article  CAS  Google Scholar 

  12. Naidich DP, Rusinek H, McGuinness G, Leitman B, McCauley DI, Henschke CI (1993) Variables affecting pulmonary nodule detection with computed tomography: evaluation with three-dimensional computer simulation. J Thorac Imaging 8:291–299

    Article  PubMed  CAS  Google Scholar 

  13. Henschke CI, McCauley DI, Yankelevitz DF et al (2001) Early lung cancer action project: a summary of the findings on baseline screening. Oncologist 6:147–152

    Article  PubMed  CAS  Google Scholar 

  14. Diederich S, Wormanns D, Semik M et al (2002) Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 222:773–781

    Article  PubMed  Google Scholar 

  15. Swensen SJ, Jett JR, Hartman TE et al (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226:756–761

    Article  PubMed  Google Scholar 

  16. Swensen SJ, Jett JR, Sloan JA et al (2002) Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 165:508–513

    PubMed  Google Scholar 

  17. Leader JK, Warfel TE, Fuhrman CR et al (2005) Pulmonary nodule detection with low-dose CT of the lung: agreement among radiologists. AJR Am J Roentgenol 185:973–978

    Article  PubMed  Google Scholar 

  18. Marten K, Auer F, Schmidt S, Kohl G, Rummeny EJ, Engelke C (2006) Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria. Eur Radiol 16:781–790, Epub 2005 Dec 6

    Article  PubMed  Google Scholar 

  19. Erasmus JJ, Gladish GW, Broemeling L et al (2003) Interobserver and intraobserver variability in measurement of non-small cell carcinoma of the lung lesions: implications for assessment of tumour response. J Clin Oncol 21:2574–2582

    Article  PubMed  Google Scholar 

  20. Fiebich M, Wietholt C, Renger BC et al (1999) Automatic detection of pulmonary nodules in low-dose scrreening thoracic CT examinations. Proc SPIE 3661:1434–1439

    Article  Google Scholar 

  21. Fan L, Novak CL, Qian J, Kohl G, Naidich DP (2001) Automatic detection of lung nodules from multi-slice low-dose CT images. Proc SPIE 4322:1828–1835

    Article  Google Scholar 

  22. Satoh H, Ukai Y, Niki N et al (1999) Computer aided diagnosis system for lung cancer based on retrospective helical CT images. Proc SPIE 3661:1324–1335

    Article  Google Scholar 

  23. Okumura T, Miwa T, Kako J et al (1998) Image processing for computer-aided diagnosis of lung cancer screening system by CT (LSCT). Proc SPIE 3338:1314–1322

    Article  Google Scholar 

  24. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved tmplated matching technique. IEEE Trans Med Imaging 20:595–604

    Article  PubMed  CAS  Google Scholar 

  25. Giger ML, Bae KT, MacMahon H (1994) Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 29:459–465

    Article  PubMed  CAS  Google Scholar 

  26. Zhao B, Gamsu G, Ginsber MS, Jiang L, Schwartz LH (2003) Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Medi Phys 4:248–260

    Article  Google Scholar 

  27. Lou S, Chang C, Lin K, Chen T (1999) Object-based deformation technique for 3-D CT lung nodule detection. Proc SPIE 3661:1544–1552

    Article  Google Scholar 

  28. Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR (2001) Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans Med Imaging 20:1242–1250

    Article  PubMed  CAS  Google Scholar 

  29. Taguchi H, Kawata Y, Niki N et al (1999) Lung cancer detection based on helical CT images using curved surface morphology analysis. Proc SPIE 3661:1307–1314

    Article  Google Scholar 

  30. Croisille P, Souto M, Cova M et al (1995) Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT. Radiology 197:397–401

    PubMed  CAS  Google Scholar 

  31. Armato SG, 3rd, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19:1303–1311

    PubMed  Google Scholar 

  32. Sohaib SA, Turner B, Hanson JA, Farquharson M, Oliver RTD, Reznek RH (2000) CT assessment of tumour response to treatment: comparison of linear, cross-sectional and volumetric measures of tumour size. Br J Radiol 73:1178–1184

    PubMed  CAS  Google Scholar 

  33. Prasad SR, Jhaveri KS, Saini S, Hahn PF, Halpern EF, Sumner JE (2002) CT tumour response assessment: comparison of unidimensional, bidimensional and volumetric techniques - initial observations. Radiology 225:416–419

    Article  PubMed  Google Scholar 

  34. Wormanns D, Kohl G, Klotz E et al (2004) Volumetric measurement of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 14:86–92

    Article  PubMed  Google Scholar 

  35. Fan L, Quian J, Odry BL et al (2002) Automatic segmentation of pulmonary nodules by using dynamic cross-correlation for interactive CAD systems. In: Sonka M, Fitzpatrick JM (eds) Medical imaging: image processing. Proc SPIE 2682:1362–1396

  36. Ko JP, Betke M (2001) Chest CT: automated nodule detection and assessment of change over time - preliminary experience. Radiology 218:267–273

    PubMed  CAS  Google Scholar 

  37. Wormanns D, Fiebich M, Saidi M, Diederich S, Heindel W (2002) Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system. Eur Radiol 12:1052–1057

    Article  PubMed  Google Scholar 

  38. Awai K, Murao K, Ozawa A et al (2004) Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’detection performance. Radiology 230:347–352

    Article  PubMed  Google Scholar 

  39. Giger M, MacMahon H (1996) Image processing and computer-aided diagnosis. Radiol Clin North Am 34:565–596

    PubMed  CAS  Google Scholar 

  40. Novak CL, Fan L, Quian J et al (2003) Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system. SPIE 5034:439–447

    Article  Google Scholar 

  41. Wormanns D, Beyer F, Diederich S, Ludwig K, Heindel W (2004) Diagnostic performance of a commercially available computer-aided diagnosis system for automatic detection of pulmonary nodules: comparison with single and double reading. Fortschr Röntgenstr 176:953–958

    Article  CAS  Google Scholar 

  42. Rubin G, Lyo J, Paik D, Sherbondy A, Naidich DP, Napel S (2003) Impact of computer-assisted detection (CAD) algorithm vs a second radiologist on reader sensitivity for detecting pulmonary nodules in MDCT scans. Radiology S293 [abstract]

  43. Brown MS, Goldin JG, Rogers S et al (2005) Computer-aided lung nodule detection in CT: results of large-scale observer test. Acad Radiol 12:681–686

    Article  PubMed  Google Scholar 

  44. Kim J-S, Kim J-H, Cho G, Bae KT (2005) Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval - initial results. Radiology 236:295–299

    Article  PubMed  Google Scholar 

  45. Peldschus K, Martensen J, Cheema J, Miao M, Wood S, Schoepf U (2003) Computer-aided diagnosis of focal lung disease with dedicated visualization tools and automated lesion detection: influence on reader effectiveness. Radiology S293 [abstract]

  46. Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI (2004) Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 231:446–452

    Article  PubMed  Google Scholar 

  47. Yankelevitz DF, Gupta R, Zhao B, Henschke CI (1999) Small pulmonary nodules: evaluation with repeat CT-preliminary experience. Radiology 212:561–566

    PubMed  CAS  Google Scholar 

  48. Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT (2005) Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology 235:850–856

    Article  PubMed  Google Scholar 

  49. Nathan MH, Collins VP, Adams RA (1962) Differentiation of benign and malignant pulmonary nodules by growth rate. Radiology 79:221–232

    PubMed  CAS  Google Scholar 

  50. Collins VP, Loeffler RK, Tivey H (1956) Observations on growth rates of human tumours. Am J Roentgenol Radium Ther Nucl Med 76:988–1000

    PubMed  CAS  Google Scholar 

  51. Winer-Muram HT, Jennings SG, Tarver RD et al (2002) Volumetric growth rate of stage I lung cancer prior to treatment: serial CT scanning. Radiology 223:798–805, Jun

    Article  PubMed  Google Scholar 

  52. Hasegawa M, Sone S, Takashima S et al (2000) Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 73:1252–1259

    PubMed  CAS  Google Scholar 

  53. Miller AB, Hogestraeten B, Staquet M, Winkler A (1981) Reporting results of cancer treatment. Cancer 47:207–214

    Article  PubMed  CAS  Google Scholar 

  54. Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumours. J Natl Cancer Inst 92:205–216

    Article  PubMed  CAS  Google Scholar 

  55. Ratain M (2004) Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. J Clin Oncol 22:4442–4444

    Article  PubMed  Google Scholar 

  56. Thiesse P, Ollivier L, Di Stefano-Louineau D et al (1997) Response rate accuracy in oncology trials: reasons for interobserver variability. J Clin Oncol 15:3507–3514

    PubMed  CAS  Google Scholar 

  57. Tran LN, Browhn MS, Goldin JG et al (2004) Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography. Acad Radiol 11:1355–1360

    Article  PubMed  Google Scholar 

  58. Werner-Wasik M, Xiao Y, Pequignot E, Curran WJ, Hauck W (2001) Assessment of lung cancer response after nonoperative therapy: tumour diameter, bidimensional product, and volume. A serial CT scan-based study. Int J Radiat Oncol Biol Phys 51:56–61

    PubMed  CAS  Google Scholar 

  59. Pastorino U, Bellomi M, Landoni C et al (2003) Early lung-cancer detection with spiral CT and positron emission tomography in heavy smokers: 2-year results. Lancet 362:593–597

    Article  PubMed  Google Scholar 

  60. Sobue T, Moriyama N, Kaneko M et al (2002) Screening for lung cancer with low-dose helical computed tomography: Anti-Lung Cancer Association Project. J Clin Oncol 20:911–920

    Article  PubMed  Google Scholar 

  61. Okada K, Comanicou D, Krishnan A (2005) Robust anisotropic gaussian fitting for volumetric characterization of pulmonary nodules in multislice-CT. IEEE Trans MEd Imag 24:409–423

    Article  Google Scholar 

  62. Kim KG, Goo JM, Kim JH et al (2005) Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 237:657–661

    Article  PubMed  Google Scholar 

  63. Goodman LR, Washington L, Nagy PG, Piacsek KL (2006) Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol 186:989–994

    Article  PubMed  Google Scholar 

  64. Kuhnigk J-M, Dicken V, Bornemann L et al (2006) Morphological sgementation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imag 25:417–434

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany

    Katharina Marten & Christoph Engelke

Authors
  1. Katharina Marten
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christoph Engelke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katharina Marten.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Marten, K., Engelke, C. Computer-aided detection and automated CT volumetry of pulmonary nodules. Eur Radiol 17, 888–901 (2007). https://doi.org/10.1007/s00330-006-0410-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-006-0410-3

Keywords

Search

Navigation