Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

Medical Image Analysis

Volumn 18, Issue 2, 2014, Pages 374-384

  Jacobs, Colin ^a,b   van Rikxoort, Eva M ^a,b   Twellmann, Thorsten ^b   Scholten, Ernst Th ^c,d   de Jong, Pim A ^c   Kuhnigk, Jan Martin ^b   Oudkerk, Matthijs ^e   de Koning, Harry J ^f   Prokop, Mathias ^a   Schaefer Prokop, Cornelia ^a,g   van Ginneken, Bram ^a,b  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b FRAUNHOFER MEVIS   (Germany)

^c UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^d Utrecht University Medical Center ^*  (Netherlands)

^e UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^f ERASMUS MEDICAL CENTER   (Netherlands)

^g MEANDER MEDICAL CENTER   (Netherlands)

Author keywords

Computed tomography (CT); Computer aided detection (CAD); Lung cancer; Lung nodule; Subsolid nodule

Indexed keywords

CLASSIFICATION PERFORMANCE; COMPUTED TOMOGRAPHY IMAGES; COMPUTER-AIDED DETECTION; FALSE POSITIVE DETECTION; LUNG CANCER; LUNG NODULE; RETROSPECTIVE ANALYSIS; SUBSOLID NODULE;

BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; DISEASES; EXPERIMENTS; OPTIMIZATION;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DISEASE CLASSIFICATION; FALSE POSITIVE RESULT; HUMAN; IMAGE ANALYSIS; LUNG NODULE; MAJOR CLINICAL STUDY; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RADIOLOGIST; RETROSPECTIVE STUDY; SENSITIVITY ANALYSIS; SUBSOLID PULMONARY NODULE; ALGORITHM; AUTOMATED PATTERN RECOGNITION; CLINICAL TRIAL; EARLY DIAGNOSIS; LUNG TUMOR; MULTICENTER STUDY; PROCEDURES; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; EARLY DETECTION OF CANCER; HUMANS; LUNG NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84892452298     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.12.001     Document Type: Article

References (48)

1. Aberle D.R., Adams A.M., Berg C.D., Black W.C., Clapp J.D., Fagerstrom R.M., Gareen I.F., Gatsonis C., Marcus P.M., Sicks J.D. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 2011, 365:395-409.
2. American Cancer Society, 2012. Cancer Facts and Figs. 2012.
3. Armato S.G., McLennan G., Bidaut L., McNitt-Gray M.F., Meyer C.R., Reeves A.P., Zhao B., Aberle D.R., Henschke C.I., Hoffman E.A., Kazerooni E.A., MacMahon H., Beek E.J.R.V., Yankelevitz D., Biancardi A.M., Bland P.H., Brown M.S., Engelmann R.M., Laderach G.E., Max D., Pais R.C., Qing D.P.Y., Roberts R.Y., Smith A.R., Starkey A., Batrah P., Caligiuri P., Farooqi A., Gladish G.W., Jude C.M., Munden R.F., Petkovska I., Quint L.E., Schwartz L.H., Sundaram B., Dodd L.E., Fenimore C., Gur D., Petrick N., Freymann J., Kirby J., Hughes B., Casteele A.V., Gupte S., Sallamm M., Heath M.D., Kuhn M.H., Dharaiya E., Burns R., Fryd D.S., Salganicoff M., Anand V., Shreter U., Vastagh S., Croft B.Y. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 2011, 38:915-931.
4. Beigelman-Aubry C., Hill C., Boulanger X., Brun A., Leclercq D., Golmard J., Grenier P., Lucidarme O. Evaluation of a computer aided detection system for lung nodules with ground glass opacity component on multidetector-row CT. J. Radiol. 2009, 90:1843-1849.
5. Bellotti R., Carlo F.D., Gargano G., Tangaro S., Cascio D., Catanzariti E., Cerello P., Cheran S.C., Delogu P., Mitri I.D., Fulcheri C., Grosso D., Retico A., Squarcia S., Tommasi E., Golosio B. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Med. Phys. 2007, 34:4901-4910.
6. Breiman L. Random forests. Machine Learn. 2001, 45:5-32.
7. Cover T., Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13:21-27.
8. Dehmeshki J., Ye X., Lin X., Valdivieso M., Amin H. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imag. Graph. 2007, 31:408-417.
9. van Dongen, E., van Ginneken, B., 2010. Automatic segmentation of pulmonary vasculature in thoracic CT scans with local thresholding and airway wall removal. In: IEEE Int. Symp. Biomedical Imaging, pp. 668-671.
10. Enquobahrie A.A., Reeves A.P., Yankelevitz D.F., Henschke C.I. Automated detection of small pulmonary nodules in whole lung CT scans. Acad. Radiol. 2007, 14:579-593.
11. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: Med. Image Comput. Comput. Assist. Interv., pp. 130-137.
12. Friedman J., Hastie T., Tibshirani R. Special invited paper. additive logistic regression: a statistical view of boosting. Ann. Stat. 2000, 28:337-374.
13. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press. second ed.
14. van Ginneken B., Armato S.G., de Hoop B., van de Vorst S., Duindam T., Niemeijer M., Murphy K., Schilham A.M.R., Retico A., Fantacci M.E., Camarlinghi N., Bagagli F., Gori I., Hara T., Fujita H., Gargano G., Belloti R., Carlo F.D., Megna R., Tangaro S., Bolanos L., Cerello P., Cheran S.C., Torres E.L., Prokop M. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med. Image Anal. 2010, 14:707-722.
15. van Ginneken, B., Baggerman, W., van Rikxoort, E.M., 2008. Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans. In: Med. Image Comput. Comput. Assist. Interv., pp. 219-226.
16. Godoy M.C.B., Naidich D.P. Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology 2009, 253:606-622.
17. Hansell D.M., Bankier A.A., MacMahon H., McLoud T.C., Müller N.L., Remy J. Fleischner society: glossary of terms for thoracic imaging. Radiology 2008, 246:697-722.
18. Heitmann K.R., Kauczor H., Mildenberger P., Uthmann T., Perl J., Thelen M. Automatic detection of ground glass opacities on lung HRCT using multiple neural networks. Eur. Radiol. 1997, 7:1463-1472.
19. Henschke C.I., McCauley D.I., Yankelevitz D.F., Naidich D.P., McGuinness G., Miettinen O.S., Libby D.M., Pasmantier M.W., Koizumi J., Altorki N.K., Smith J.P. Early lung cancer action project: overall design and findings from baseline screening. Lancet 1999, 354:99-105.
20. Henschke C.I., Yankelevitz D.F., Mirtcheva R., McGuinness G., McCauley D., Miettinen O.S. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am. J. Roentgenol. 2002, 178:1053-1057.
21. Hu M.K. Visual pattern recognition by moment invariants, computer methods in image analysis. IRE Trans. Inform. Theory 1962, 8:179-187.
22. Hupse, R., Karssemeijer, N., 2009. The use of contextual information for computer aided detection of masses in mammograms. In: Medical Imaging, p. 72600Q.
23. Jacobs, C., Sánchez, C.I., Saur, S.C., Twellmann, T., de Jong, P.A., van Ginneken, B., 2011. Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features. In: Med. Image Comput. Comput. Assist. Interv., pp. 207-214.
24. Jobson J.D. Applied Multivariate Data Analysis 1992, Springer-Verlag.
25. Kauczor H.U., Heitmann K., Heussel C.P., Marwede D., Uthmann T., Thelen M. Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. AJR Am. J. Roentgenol. 2000, 175:1329-1334.
26. Kim K.G., Goo J.M., Kim J.H., Lee H.J., Min B.G., Bae K.T., Im J.G. Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 2005, 237:657-661.
27. van Klaveren R.J., Oudkerk M., Prokop M., Scholten E.T., Nackaerts K., Vernhout R., van Iersel C.A., van den Bergh K.A.M., van 't Westeinde S., van der Aalst C., Thunnissen E., Xu D.M., Wang Y., Zhao Y., Gietema H.A., de Hoop B., Groen H.J.M., de Bock G.H., van Ooijen P., Weenink C., Verschakelen J., Lammers J.J., Timens W., Willebrand D., Vink A., Mali W., de Koning H.J. Management of lung nodules detected by volume CT scanning. N. Engl. J. Med. 2009, 361:2221-2229.
28. Kuhnigk J.M., Dicken V., Bornemann L., Bakai A., Wormanns D., Krass S., Peitgen H.O. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans. Med. Imag. 2006, 25:417-434.
29. Li Q., Li F., Doi K. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad. Radiol. 2008, 15:165-175.
30. Marten K., Engelke C. Computer-aided detection and automated CT volumetry of pulmonary nodules. Eur. Radiol. 2007, 17:888-901.
31. Messay T., Hardie R.C., Rogers S.K. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 2010, 14:390-406.
32. Murphy K., van Ginneken B., Schilham A.M.R., de Hoop B.J., Gietema H.A., Prokop M. A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 2009, 13:757-770.
33. Naidich D.P., Bankier A.A., MacMahon H., Schaefer-Prokop C.M., Pistolesi M., Goo J.M., Macchiarini P., Crapo J.D., Herold C.J., Austin J.H., Travis W.D. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the fleischner society. Radiology 2013, 266:304-317.
34. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Machine Intell. 2002, 24:971-987.
35. Peldschus K., Herzog P., Wood S.A., Cheema J.I., Costello P., Schoepf U.J. Computer-aided diagnosis as a second reader: spectrum of findings in CT studies of the chest interpreted as normal. Chest 2005, 128:1517-1523.
36. Pudil P., Novovicova J., Kittler J. Floating search methods in feature selection. Pattern Recognit. Lett. 1994, 15:1119-1125.
37. Retico A., Delogu P., Fantacci M.E., Gori I., Martinez A.P. Lung nodule detection in low-dose and thin-slice computed tomography. Comput. Biol. Med. 2008, 38:525-534.
38. van Rikxoort E.M., de Hoop B., Viergever M.A., Prokop M., van Ginneken B. Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Med. Phys. 2009, 36:2934-2947.
39. Sánchez C.I., Niemeijer M., Isgum I., Dumitrescu A.V., Suttorp-Schulten M.S.A., Abràmoff M.D., van Ginneken B. Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans. Med. Image Anal. 2012, 16:50-62.
40. Song Y., Cai W., Kim J., Feng D. A multi-stage discriminative model for tumor and lymph node detection in thoracic images. IEEE Trans. Med. Imag. 2012, 31:1061-1075.
41. Sörensen, L., Shaker, S.B., de Bruijne, M., 2008. Texture classification in lung CT using local binary patterns. In: Med. Image. Comput. Comput. Assist. Interv., pp. 934-941.
42. Sousa J.R., Silva A.C., de Paiva A.C., Nunes R.A. Methodology for automatic detection of lung nodules in computerized tomography images. Comput. Methods Programs Biomed. 2009, 98:1-14.
43. Tan M., Deklerck R., Jansen B., Bister M., Cornelis J. A novel computer-aided lung nodule detection system for CT images. Med. Phys. 2011, 38:5630-5645.
44. Tao, Y., Lu, L., Dewan, M., Chen, A.Y., Corso, J., Xuan, J., Salganicoff, M., Krishnan, A., 2009. Multi-level ground glass nodule detection and segmentation in CT lung images. In: Med. Image Comput. Comput. Assist. Interv., pp. 715-723.
45. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
46. Ye, X., Lin, X., Beddoe, G., Dehmeshki, J., 2007. Efficient computer-aided detection of ground-glass opacity nodules in thoracic CT images. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 4449-4452.
47. Ye X., Lin X., Dehmeshki J., Slabaugh G., Beddoe G. Shape based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng. 2009, 56:1810-1820.
48. Zhou, J., Chang, S., Metaxas, D.N., Zhao, B., Ginsberg, M.S., Schwartz, L.H., 2006. An automatic method for ground glass opacity nodule detection and segmentation from CT studies. In: IEEE EMBS, pp. 3062-3065.