Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning

Radiological Physics and Technology

Volumn 10, Issue 1, 2017, Pages 23-32

  van Ginneken, Bram ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Computer aided detection; Computer aided diagnosis; Deep learning; Image processing; Machine learning; Pulmonary image analysis

Indexed keywords

ALGORITHM; BRONCHOSCOPY; CHRONIC OBSTRUCTIVE LUNG DISEASE; COMPUTER AIDED DESIGN; COMPUTER ASSISTED TOMOGRAPHY; DEEP LEARNING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGING SOFTWARE; INFORMATION PROCESSING; LIMIT OF QUANTITATION; MACHINE LEARNING; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; REVIEW; THORAX RADIOGRAPHY; DIAGNOSTIC IMAGING; LUNG TUMOR; PROCEDURES;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG NEOPLASMS; MACHINE LEARNING; RADIOGRAPHY, THORACIC;

EID: 85013130699     PISSN: 18650333     EISSN: 18650341     Source Type: Journal    
DOI: 10.1007/s12194-017-0394-5     Document Type: Review

References (62)

1. Lodwick GS. Computer-aided diagnosis in radiology. a research plan. Invest Radiol. 1966;1:72–80.
2. Lodwick GS, Keats TE, Dorst JP. The coding of Roentgen images for computer analysis as applied to lung cancer. Radiology. 1963;81:185–200.
3. Toriwaki J, Suenaga Y, Negoro T, Fukumura T. Pattern recognition of chest X-ray images. Computer Gr Image Process. 1973;2:252–71.
4. Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007;31:198–211.
5. Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2001.
6. Bishop CM. Pattern recognition and machine learning (information science and statistics). Secaucus: Springer; 2007. ISBN 0387310738.
7. van Ginneken B, ter Haar Romeny BM, Viergever MA. Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging. 2001;20:1228–41.
8. Sluimer IC, van Waes PF, Viergever MA, van Ginneken B. Computeraided diagnosis in high-resolution CT of the lungs. Med Phys. 2003;30:3081–90.
9. van Ginneken B, Hogeweg L, Prokop M. Computer-aided diagnosis in chest radiography: beyond nodules. Eur J Radiol. 2009;72:226–30.
10. van Rikxoort EM, van Ginneken B. Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys Med Biol. 2013;58:R187–220.
11. van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology. 2011;261(3):719–32.
12. Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern. 1980;36:193–202.
13. Lo S-CB, Lou S-LA, Lin J-S, Freedman MT, Chien MV, Mun SK. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging. 1995;14:711–8.
14. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–324.
15. Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Advs Neural Inf Process Syst. 2012;25:1097–105.
16. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M. Imagenet large scale visual recognition challenge. Int J Comput Vision. 2014;115(3):1–42.
17. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.
18. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
19. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798–828.
20. Charbonnier JP, van Rikxoort EM, Setio AAA, Schaefer-Prokop C, van Ginneken B, Ciompi F. Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal. 2017;36:52–60.
21. Nair V, Hinton G. Rectified linear units improve restricted Boltzmann machines. In: International conference on machine learning; 2010. pp. 807–14.
22. Greenspan H, Summers RM, van Ginneken B. Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016;35(5):1153–9.
23. Vogelsang F, Weiler F, Dahmen J, Kilbinger MW, Wein B, Gu¨nther RW. Detection and compensation of rib structures in chest radiographs for diagnose assistance. In: Medical imaging, vol. 3338 of proceedings of the SPIE; 1998. pp. 774–85.
24. van Ginneken B, ter Haar Romeny BM. Automatic delineation of ribs in frontal chest radiographs. In: Medical imaging, vol 3979 of Proceedings of the SPIE; 2000. pp. 825–36.
25. Loog M, van Ginneken B. Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans Med Imaging. 2006;25:602–11.
26. L. Hogeweg, C. Mol, P. A. de Jong, and B. van Ginneken. Rib suppression in chest radiographs to improve classification of textural abnormalities. In: Medical imaging, vol 7624 of proceedings of the SPIE; 2010. pp. 76240Y1–Y6.
27. Hogeweg L, Sánchez CI, van Ginneken B. Suppression of translucent elongated structures: applications in chest radiography. IEEE Trans Med Imaging. 2013;32:2099–113.
28. Suzuki K, Abe H, MacMahon H, Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging. 2006;25:406–16.
29. Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol. 2005;12:191–201.
30. Suzuki K, Armato SG, Li F, Sone S, Doi K. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys. 2003;30:1602–17.
31. Loog M, van Ginneken B, Schilham AMR. Filter learning: application to suppression of bony structures from chest radiographs. Med Image Anal. 2006;10:826–40.
32. Yang W, Chen Y, Liu Y, Zhong L, Qin G, Lu Z, Feng Q, Chen W. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med Image Anal. 2016;35:421–33.
33. 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. 2012;265:611–6.
34. van Rikxoort EM, Goldin JG, Abtin F, Kim HJ, Lu P, van Ginneken B, Shaw G, Galperin-Aizenberg M, Brown MS. A method for the automatic quantification of the completeness of pulmonary fissures: evaluation in a database of subjects with severe emphysema. Eur Radiol. 2012;22:302–9.
35. van Rikxoort EM, van Ginneken B, Klik M, Prokop M. Supervised enhancement filters: application to fissure detection in chest CT scans. IEEE Trans Med Imaging. 2008;27:1–10.
36. Wiemker R, Bu¨low T, Blaffert T. Unsupervised extraction of the pulmonary interlobar fissures from high resolution thoracic CT data. In: Computer assisted radiology and surgery, vol 1281 of international congress series; 2005. pp. 1121–26.
37. Agam G, Armato SG III, Wu C. Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005;24:486–99.
38. Ochs RA, Goldin JG, Abtin F, Kim HJ, Brown K, Batra P, Roback D, McNitt-Gray MF, Brown MS. Automated classification of lung bronchovascular anatomy in CT using AdaBoost. Med Image Anal. 2007;11:315–24.
39. van Ginneken B, Baggerman, van Rikxoort EM. Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans. In: Medical image computing and computer-assisted intervention, vol 5241 of lecture notes in computer science; 2008. pp. 219–26.
40. Schlathölter T, Lorenz C, Carlsen IC, Renisch S, Deschamps T. Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. In: Medical imaging, vol 4684 of Proceedings of the SPIE; 2002. pp. 103–13.
41. Kiraly AP, Pichon E, Naidich DP, Novak CL. Analysis of arterial sub-trees affected by pulmonary emboli. In: Medical Imaging, vol 5370 of proceedings of the SPIE; 2004. pp. 1720–29.
42. Lo P, van Ginneken B, Reinhardt JM, Tarunashree Y, de Jong PA, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijanska A, Bauer C, Beichel R, Mendoza CS, Wiemker R, Lee J, Reeves AP, Born S, Weinheimer O, van Rikxoort EM, Tschirren J, Mori K, Odry B, Naidich DP, Hartmann IJ, Hoffman EA, Prokop M, Pedersen JH, de Bruijne M. Extraction of airways from CT (EXACT’09). IEEE Trans Med Imaging. 2012;31:2093–107.
43. Lo P, Sporring J, Ashraf H, Pedersen JJH, de Bruijne M. Vessel-guided airway tree segmentation: a voxel classification approach. Med Image Anal. 2010;14:527–38.
44. Wiemker R, Rogalla P, Zwartkruis A, Blaffert T. Computer aided lung nodule detection on high resolution CT data. In: Medical imaging, volume 4684 of proceedings of the SPIE; 2002. pp. 677–88.
45. Murphy K, van Ginneken B, Schilham AMR, de Hoop BJ, Gietema HA, 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(5):757–70.
46. van Ginneken B, Armato SG, de Hoop B, van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham AMR, Retico A, Fantacci ME, Camarlinghi N, Bagagli F, Gori I, Hara T, Fujita H, Gargano G, Belloti R, De Carlo F, Megna R, Tangaro S, Bolanos L, Cerello P, Cheran SC, Lopez Torres E, Prokop M. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal. 2010;14:707–22.
47. Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DPY, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Vande Casteele A, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medi Phys. 2011;38:915–31.
48. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel S, Winkler Wille M, Naqibullah M, Sanchez C, van Ginneken B. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging. 2016;35(5):1160–9.
49. Bergtholdt M, Wiemker R, Klinder T. Pulmonary nodule detection using a cascaded SVM classifier. In: Medical imaging, vol 9785 of proceedings of the SPIE; 2016. p. 978513.
50. Setio AAA, Traverso A, de Bel T, Berens MSN, van den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, van der Gugten R, Heng PA, Jansen B, de Kasten MMJ, Kotov V, Yu-Hung Lin J, Manders JTMC, So´nora-Mengana A, Carlos Garc´ıa-Naranjo J, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ETh, Scholten L, Snoeren M, Lopez Torres E, Vandemeulebroucke J, Walasek N, Zuidhof GCA, van Ginneken B, Jacobs C. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. 2016. arXiv:1612.08012.
51. McNitt-Gray MF, Hart EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999;26:880–8.
52. Suzuki K. Machine learning in computer-aided diagnosis of the thorax and colon in CT: a survey. IEICE Trans Inf Syst. 2013;96:772–83.
53. Suzuki K, Li F, Sone S, Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imaging. 2005;24:1138–50.
54. McWilliams A, Tammemagi MC, Mayo JR, Roberts H, Liu G, Soghrati K, Yasufuku K, Martel S, Laberge F, Gingras M, Atkar-Khattra S, Berg CD, Evans K, Finley R, Yee J, English J, Nasute P, Goffin J, Puksa S, Stewart L, Tsai, Johnston MR, Manos D, Nicholas G, Goss GD, Seely JM, Amjadi K, Tremblay A, Burrowes P, MacEachern P, Bhatia R, Tsao M, Lam S. Probability of cancer in pulmonary nodules detected on first screening CT. Engl J Med. 2013;369:910–9.
55. Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, Scholten ETH, Schaefer-Prokop CM, Wille MMW, Marchiano A, Pastorino U, Prokop M, van Ginneken B. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. 2016. arXiv:1610.09157.
56. Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen HO. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging. 2006;25:417–34.
57. Lassen BC, Jacobs C, Kuhnigk J-M, van Ginneken B, van Rikxoort EM. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol. 2015;60:1307–23.
58. Gallardo-Estrella L, Lynch DA, Prokop M, Stinson D, Zach J, Judy PF, van Ginneken B, van Rikxoort EM. Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification. Eur Radiol. 2016;26:478–86.
59. Lassen B, van Rikxoort EM, Schmidt M, Kerkstra S, van Ginneken B, Kuhnigk J. Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE Trans Med Imaging. 2013;32:210–22.
60. Shen W, Zhou M, Yang F, Yang C, Tian J. Multi-scale convolutional neural networks for lung nodule classification. In: Information processing in medical imaging, vol 9123 of lecture notes in computer science; 2015. pp. 588–99.
61. Shin H-C, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. Learning to read chest X-rays: Recurrent neural cascade model for automated image annotation;2016. arXiv:1603.08486.
62. Wang X, Lu L, Shin H, Kim L, Nogues I, Yao J, Summers RM. Unsupervised category discovery via looped deep pseudo-task optimization using a large scale radiology image database;2016. arXiv:1603.07965.