Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography

Medical Physics

Volumn 30, Issue 7, 2003, Pages 1602-1617

  Suzuki, Kenji ^a   Armato III, Samuel G ^a   Li, Feng ^a   Sone, Shusuke ^a,b   Doi, Kunio ^a  

^a UNIVERSITY OF CHICAGO   (United States)

^b Azumi General Hospital   (Japan)

Author keywords

Artificial neural network; Computer aided diagnosis (CAD); False positive; Lung nodule; Pattern recognition

Indexed keywords

BIOLOGICAL ORGANS; COMPUTER AIDED DIAGNOSIS; DISEASES; NEURAL NETWORKS; PATTERN RECOGNITION;

COMPUTER AIDED DIAGNOSIS(CAD); DETECTION OF LUNG NODULES; FALSE POSITIVE; FALSE POSITIVE RATES; LUNG CANCER SCREENING; LUNG NODULE; LUNG NODULE DETECTION; PATTERN RECOGNITION TECHNIQUES;

COMPUTERIZED TOMOGRAPHY;

ADULT; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER SCREENING; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; FEMALE; HUMAN; IMAGE PROCESSING; LUNG CANCER; LUNG NODULE; MAJOR CLINICAL STUDY; MALE; PATTERN RECOGNITION; PRIORITY JOURNAL; SAMPLING; TISSUE SECTION; TRAINING;

EID: 0038710369     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.1580485     Document Type: Article

References (47)

1. A. Jemal, A. Thomas, T. Murray, and M. Thun, "Cancer statistics, 2002," Ca-Cancer J. Clin. 52, 23-47 (2002).
2. R. T. Heelan, B. J. Flehinger, M. R. Melamed, M. B. Zaman, W. B. Perchick, J. F. Caravelli, and N. Martini, "Non-small-cell lung cancer: Results of the New York screening program," Radiology 151, 289-293 (1984).
3. S. Sone et al., "Mass screening for lung cancer with mobile spiral computed topography scanner," Lancet 351, 1242-1245 (1998).
4. M. Kaneko, K. Eguchi, H. Ohmatsu, R. Kakinuma, T. Naruke, K. Suemasu, and N. Moriyama, "Peripheral lung cancer: Screening and detection with low-dose spiral CT versus radiography," Radiology 201, 798-802 (1996).
5. C. I. Henschke et al., "Early lung cancer action project: Overall design and findings from baseline screening," Lancet 354, 99-105 (1999).
6. J. W. Gurney, "Missed lung cancer at CT: Imaging findings in nine patients," Radiology 199, 117-122 (1996).
7. F. Li, S. Sone, H. Abe, H. MacMahon, S. G. Armato III, and K. Doi, "Lung cancers missed at low-dose helical CT screening in a general population: Comparison of clinical, histopathologic, and image findings," Radiology 225, 673-683 (2002).
8. S. Yamamoto, I. Tanaka, M. Senda, Y. Tateno, T. Iinuma, T. Matsumoto, and M. Matsumoto, "Image processing for computer-aided diagnosis of lung cancer by CT (LDCT)," Syst. Comput. Japan 25, 67-80 (1994).
9. T. Okumura, T. Miwa, J. Kako, S. Yamamoto, M. Matsumoto, Y. Tateno, T. Iinuma, and T. Matsumoto, "Image processing for computer-aided diagnosis of lung cancer screening system by CT (LDCT)," Proc. SPIE 3338, 1314-1322 (1998).
10. W. J. Ryan, J. E. Reed, S. J. Swensen, and J. P. F. Sheedy, "Automatic detection of pulmonary nodules in CT," Proc. Computer Assisted Radiology, 1996, pp. 385-389.
11. K. Kanazawa, M. Kubo, N. Niki, H. Satoh, H. Ohmatsu, K. Eguchi, and N. Moriyama, "Computer assisted lung cancer diagnosis based on helical images," Image Analysis Applications and Computer Graphics: Proc. Int. Computer Science Conf., 1995, pp. 323-330.
12. M. L. Giger, K. T. Bae, and H. MacMahon, "Computerized detection of pulmonary nodules in computed tomography images," Invest. Radiol. 29, 459-465 (1994).
13. S. G. Armato III, M. L. Giger, J. T. Blackbur, K. Doi, and H. MacMahon, "Three-dimensional approach to lung nodule detection in helical CT," Proc. SPIE 3661, 553-559 (1999).
14. S. G. Armato III, M. L. Giger, C. J. Moran, J. T. Blackbur, K. Doi, and H. MacMahon, "Computerized detection of pulmonary nodules on CT scans," Radiographics 19, 1303-1311 (1999).
15. S. G. Armato III, M. L. Giger, and H. MacMahon, "Analysis of a three-dimensional lung nodule detection method for thoracic CT scans," Proc. SPIE 3979, 103-109 (2000).
16. S. G. Armato III, M. L. Giger, and H. MacMahon, "Automated detection of lung nodules in CT scans: Preliminary results," Med. Phys. 28, 1552-1561 (2001).
17. J. P. Ko and M. Betke, "Automated nodule detection and assessment of change over time-preliminary experience," Radiology 218, 267-273 (2001).
18. S. Sone, F. Li, Z.-G. Yang, S. Takashima, Y. Maruyama, M. Hasagawa, J.-C. Wang, S. Kawakami, and T. Honda, "Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner," Br. J. Cancer 84, 25-32 (2001).
19. S. G. Armato III, F. Li, M. L. Giger, H. MacMahon, S. Sone, and K. Doi, "Lung cancer: Performance of automated lung nodule detection applied to cancers missed in a CT screening program," Radiology 225, 685-692 (2002).
20. K. Arakawa and H. Harashima, "A nonlinear digital filter using multilayered neural networks," Proc. IEEE Int. Conf. Commun. 2, 424-428 (1990).
21. L. Yin, J. Astola, and Y. Neuvo, "A new class of nonlinear filters - neural filters," IEEE Trans. Signal Process. 41, 1201-1222 (1993).
22. L. Yin, J. Astola, and Y. Neuvo, "Adaptive multistage weighted order statistic filters based on the back propagation algorithm," IEEE Trans. Signal Process. 42, 419-422 (1994).
23. H. Hanek and N. Ansari, "Speeding up the generalized adaptive neural filters," IEEE Trans. Image Process. 5. 705-712 (1996).
24. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "A recurrent neural filter for reducing noise in medical x-ray image sequences," Proc. Int. Conf. Neural Information Processing 1, 157-160 (1998).
25. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "Noise reduction of medical x-ray image sequences using a neural filter with spatiotemporal inputs," Proc. Int. Symp. Noise Reduction for Imaging and Communication Systems, 1998, pp. 85-90.
26. K. Suzuki, I. Horiba, and N. Sugie, "Training under achievement quotient criterion," Neural Networks for Signal Processing X (IEEE, Piscataway, NJ, 2000), pp. 537-546.
27. K. Suzuki, I. Horiba, and N. Sugie, "Signal preserving training for neural networks for signal processing," Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems 1, 292-297 (2000).
28. K. Suzuki, I. Horiba, and N. Sugie, "Neural filter with selection of input features and its application to image quality improvement of medical image sequences," Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems 2, 783-788 (2000).
29. K. Suzuki, I. Horiba, and N. Sugie, "Efficient approximation of a neural filter for quantum noise removal in x-ray images," IEEE Trans. Signal Process. 50, 1787-1799 (2002).
30. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "Neural filter with selection of input features and its application to image quality improvement of medical image sequences," IEICE Trans. Inf. Syst. E85-D, 1710-1718 (2002).
31. K. Suzuki, I. Horiba, and N. Sugie, "Edge detection from noisy images using a neural edge detector," Neural Networks for Signal Processing X (IEEE, Piscataway, NJ, 2000), pp. 487-496.
32. K. Suzuki, I. Horiba, and N. Sugie, "Neural edge detector-a good mimic of conventional one yet robuster against noise-," Lect. Notes Comput. Sci. 2085, 303-310 (2001).
33. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "Extraction of the contours of left ventricular cavity, according with those traced by medical doctors, from left ventriculograms using a neural edge detector," Proc. SPIE 4322, 1284-1295 (2001).
34. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "Contour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector," Syst. Comput. Japan 34, 55-69 (2003).
35. K. Suzuki, I. Horiba, K. Ikegaya, and M. Nanki, "Recognition of coronary arterial stenosis using neural network on DSA system," Syst. Comput. Japan 26, 66-74 (1995).
36. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, "Computer-aided diagnosis system for coronary artery stenosis using a neural network," Proc. SPIE 4322, 1771-1782 (2001).
37. K. Funahashi, "On the approximate realization of continuous mappings by neural networks," Neural Networks 2, 183-192 (1989).
38. A. R. Barron, "Universal approximation bounds for superpositions of a sigmoidal function," IEEE Trans. Inf. Theory 39, 930-945 (1993).
39. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations of back-propagation errors," Nature (London) 323, 533-536 (1986).
40. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel Distributed Processing (MIT, Cambridge, MA, 1986), Vol. 1, Chap. 8, pp. 318-362.
41. D. P. Chakraborty and L. H. L. Winter, "Free-response methodology: Alternate analysis and a new observer-performance experiment," Radiology 174, 873-881 (1990).
42. C. E. Metz, "ROC methodology in radiologic imaging," Invest. Radiol. 21, 720-733 (1986).
43. C. E. Metz, B. A. Herman, and J.-H. Shen, "Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data," Stat. Med. 17, 1033-1053 (1998).
44. J. A. Hanley and B. J. McNeil, "A method of comparing the areas under receiver operating characteristic curves derived from the same cases," Radiology 148, 839-843 (1983).
45. K. Suzuki, I. Horiba, and N. Sugie, "Designing the optimal structure of a neural filter," Neural Networks for Signal Processing VIII (IEEE, Piscataway, NJ, 1998), pp. 323-332.
46. K. Suzuki, I. Horiba, and N. Sugie, "A simple neural network pruning algorithm with application to filter synthesis," Neural Processing Lett. 13, 43-53 (2001).
47. K. Suzuki, I. Horiba, and N. Sugie, "Simple unit-pruning with gain-changing training," Neural Networks for Signal Processing XI (IEEE, Piscataway, NJ, 2001). pp. 153-162.