Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Charbonnier, Jean Paul ^a   Chung, Kaman ^a   Scholten, Ernst T ^a   Van Rikxoort, Eva M ^a   Jacobs, Colin ^a   Sverzellati, Nicola ^b   Silva, Mario ^b   Pastorino, Ugo ^c   Van Ginneken, Bram ^a   Ciompi, Francesco ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY HOSPITAL OF PARMA   (Italy)

^c NATIONAL CANCER INSTITUTE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; DIAGNOSTIC IMAGING; EARLY CANCER DIAGNOSIS; HUMAN; LUNG TUMOR; MULTIPLE PULMONARY NODULES; OBSERVER VARIATION; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; EARLY DETECTION OF CANCER; HUMANS; LUNG NEOPLASMS; MULTIPLE PULMONARY NODULES; OBSERVER VARIATION; REPRODUCIBILITY OF RESULTS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85040529319     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-19101-3     Document Type: Article

References (21)

1. Aberle, D. R. et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 365, 395-409 (2011).
2. American College of Radiology. Lung CT screening reporting and data system (lung-rads) (2014).
3. Naidich, D. P. et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the fleischner society. Radiology. 266, 304-317 (2013).
4. McWilliams, A. et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 369, 910-919 (2013).
5. Henschke, C. I. et al. CT screening for lung cancer: Frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 178, 1053-1057 (2002).
6. de Hoop, B., van Ginneken, B., Gietema, H. & Prokop, M. Pulmonary perifissural nodules on CT scans: Rapid growth is not a predictor of malignancy. Radiology. 265, 611-616 (2012).
7. de Hoop, B. et al. Pulmonary ground-glass nodules: increase in mass as an early indicator of growth. Radiology. 255, 199-206 (2010).
8. Mull, R. T. Mass estimates by computed tomography: physical density from CT numbers. AJR Am J Roentgenol. 143, 1101-1104 (1984).
9. Jacobs, C. et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal. 18, 374-384 (2014).
10. Messay, T., Hardie, R. C. & Tuinstra, T. R. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med Image Anal. 22, 48-62 (2015).
11. Jacobs, C. et al. Solid part-solid, or non-solid? Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system. Invest Radiol. 50, 168-173 (2015).
12. Scholten, E. T. et al. Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation. Eur Radiol. 25, 488-496 (2015).
13. Pastorino, U. et al. Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial. Eur J Cancer Prev. 21, 308-315 (2012).
14. Cohen, J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 20, 37-46 (1960).
15. van Riel, S. J. et al. Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management. Radiology. 277, 863-871 (2015).
16. Ridge, C. A. et al. Differentiating between subsolid and solid pulmonary nodules at CT: Inter-and intraobserver agreement between experienced thoracic radiologists. Radiology. 278, 888-896 (2015).
17. Gallardo-Estrella, L. et al. Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification. Eur Radiol. 26, 478-486 (2015).
18. van Dongen, E. & van Ginneken, B. Automatic segmentation of pulmonary vasculature in thoracic CT scans with local thresholding and airway wall removal. In Proc. IEEE Int. Symp. Biomed. Imag. 668-671 (2010).
19. Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21-27 (1967).
20. Breiman, L. Random forests. Machine Learning 45, 5-32 (2001).
21. Friedman, J., Hastie, T. & Tibshirani, R. Special invited paper. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28, 337-374 (2000).