Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

European Radiology

Volumn 26, Issue 7, 2016, Pages 2139-2147

  Jacobs, Colin ^a   van Rikxoort, Eva M ^a,b   Murphy, Keelin ^d   Prokop, Mathias ^a   Schaefer Prokop, Cornelia M ^a,c   van Ginneken, Bram ^a,b  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b FRAUNHOFER MEVIS   (Germany)

^c MEANDER MEDICAL CENTER   (Netherlands)

^d UNIVERSITY COLLEGE CORK   (Ireland)

Author keywords

Computer assisted diagnosis; Image interpretation, computer assisted; Lung; Lung cancer; Solitary pulmonary nodule

Indexed keywords

ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC IMAGING; FALSE POSITIVE RESULT; HUMAN; IMAGE DATABASE RESOURCE INITIATIVE; IMAGE RECONSTRUCTION; LUNG IMAGE DATABASE CONSORTIUM; LUNG NODULE; PRIORITY JOURNAL; QUALITY CONTROL; REFERENCE DATABASE; SENSITIVITY ANALYSIS; THICKNESS; COMPARATIVE STUDY; FACTUAL DATABASE; LUNG; LUNG TUMOR; MULTIPLE PULMONARY NODULES; PROCEDURES; REPRODUCIBILITY; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; X-RAY COMPUTED TOMOGRAPHY;

DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LUNG; LUNG NEOPLASMS; MULTIPLE PULMONARY NODULES; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84944568829     PISSN: 09387994     EISSN: 14321084     Source Type: Journal    
DOI: 10.1007/s00330-015-4030-7     Document Type: Article

References (22)

1. Li Q (2007) Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput Med Imaging Graph 31:248–257
2. Lee SLA, Kouzani AZ, Hu EJ (2012) Automated detection of lung nodules in computed tomography images: a review. Mach Vis Appl 23:151–163
3. Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM et al (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365:395–409
4. de Koning HJ, Meza R, Plevritis SK, Ten Haaf K, Munshi VN, Jeon J et al (2014) Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force. Ann Intern Med 160:311–320
5. Black WC, Gareen IF, Soneji SS, Sicks JD, Keeler EB, Aberle DR et al (2014) Cost-effectiveness of CT screening in the National Lung Screening Trial. N Engl J Med 371:1793–1802
6. Aberle DR, Henschke CI, McLoud TC, Boiselle PM (2012) Expert opinion: barriers to CT screening for lung cancer. J Thorac Imaging 27:208
7. Prokop M (2014) Lung cancer screening: the radiologist's perspective. Semin Respir Crit Care Med 35:91–98
8. Armato SG, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR et al (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232:739–748
9. van Ginneken B, Armato SG, de Hoop B, van de Vorst S, Duindam T, Niemeijer M et al (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14:707–722
10. Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38:915–931
11. Murphy K, van Ginneken B, Schilham AMR, de Hoop BJ, Gietema HA, Prokop M (2009) A large scale evaluation of automatic pulmonary nodule detection in chest ct using local image features and k-nearest-neighbour classification. Med Image Anal 13:757–770
12. Chakraborty DP, Berbaum KS (2004) Observer studies involving detection and localization: modeling, analysis, and validation. Med Phys 31:2313–2330
13. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–29
14. Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner society: glossary of terms for thoracic imaging. Radiology 246:697–722
15. Armato SG, Roberts RY, Kocherginsky M, Aberle DR, Kazerooni EA, Macmahon H et al (2009) Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of “truth”. Acad Radiol 16:28–38
16. Armato SG, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR et al (2007) The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Acad Radiol 14:1409–1421
17. Rubin GD, Lyo JK, Paik DS, Sherbondy AJ, Chow LC, Leung AN et al (2005) Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology 234:274–283
18. Jacobs C, van Rikxoort EM, Twellmann T, Scholten ET, de Jong PA, Kuhnigk JM et al (2014) Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal 18:374–384
19. van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K et al (2009) Management of lung nodules detected by volume CT scanning. N Engl J Med 361:2221–2222
20. Drew T, Vo ML, Olwal A, Jacobson F, Seltzer SE, Wolfe JM (2013) Scanners and drillers: characterizing expert visual search through volumetric images. J Vis 13:1–13
21. Camarlinghi N (2013) Automatic detection of lung nodules in computed tomography images: training and validation of algorithms using public research databases. Eur Phys J Plus 128:1–21
22. Tan M, Deklerck R, Cornelis J, Jansen B (2013) Phased searching with NEAT in a time-scaled framework: experiments on a computer-aided detection system for lung nodules. Artif Intell Med 59:157–167