Transfer learning improves supervised image segmentation across imaging protocols

IEEE Transactions on Medical Imaging

Volumn 34, Issue 5, 2015, Pages 1018-1030

  Van Opbroek, Annegreet ^a   Ikram, M Arfan ^a   Vernooij, Meike W ^a   De Bruijne, Marleen ^a,b  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Image Segmentation; machine learning; magnetic resonance imaging; pattern recognition; Transfer learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CEREBROSPINAL FLUID; CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION; SCANNING; SUPERVISED LEARNING;

AUTOMATIC SEGMENTATIONS; CLASSIFICATION ERRORS; CLASSIFICATION SCHEME; IMPROVE PERFORMANCE; LABELED TRAINING DATA; SUPERVISED CLASSIFICATION; SUPERVISED LEARNING APPROACHES; TRANSFER LEARNING;

IMAGE SEGMENTATION;

ARTICLE; BRAIN TISSUE; CEREBROSPINAL FLUID; CONTROLLED STUDY; GRAY MATTER; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; TRANSFER LEARNING; WHITE MATTER; AUTOMATED PATTERN RECOGNITION; BRAIN; HUMAN; MULTIPLE SCLEROSIS; PATHOLOGY; PROCEDURES; SUPPORT VECTOR MACHINE;

BRAIN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; SUPPORT VECTOR MACHINE;

EID: 84929483880     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2366792     Document Type: Article

References (44)

1. P. Anbeek et al., "Probabilistic segmentation of brain tissue in MR imaging, " Neuroimage, vol. 27, no. 4, p. 795, 2005.
2. P. Anbeek, K. L. Vincken, M. J. P. van Osch, R. H. C. Bisschops, and J. van der Grond, "Automatic segmentation of different-sized white matter lesions by voxel probability estimation, " Med. Image Anal., vol. 8, no. 3, pp. 205-215, 2004.
3. J. Ashburner and K. J. Friston, "Unified segmentation, " Neuroimage, vol. 26, no. 3, pp. 839-851, 2005.
4. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, p. 27, 2011.
5. C. A. Cocosco, A. P. Zijdenbos, and A. C. Evans, "A fully automatic and robust brain MRI tissue classification method, " Med. Image Anal., vol. 7, no. 4, pp. 513-527, 2003.
6. C. Cortes and V. Vapnik, "Support-vector networks, " Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
7. W. Dai, Q. Yang, G. R. Xue, and Y. Yu, "Boosting for transfer learning, " in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 193-200.
8. R. De Boer, H. A. Vrooman, M. A. Ikram, M. W. Vernooij, M. Breteler, A. Van Der Lugt, and W. J. Niessen, "Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods, " Neuroimage, vol. 51, no. 3, pp. 1047-1056, 2010.
9. J. C. De Groot, F. E. De Leeuw, M. Oudkerk, J. Van Gijn, A. Hofman, J. Jolles, and M. Breteler, "Periventricular cerebral white matter lesions predict rate of cognitive decline, " Ann. Neurol., vol. 52, no. 3, pp. 335-341, 2002.
10. L. R. Dice, "Measures of the amount of ecologic association between species, " Ecol., vol. 26, no. 3, pp. 297-302, 1945.
11. B. Fischl, "FreeSurfer, " Neuroimage, vol. 62, no. 2, pp. 774-781, 2012.
12. J. Folkesson, E. B. Dam, O. F. Olsen, P. C. Pettersen, and C. Christiansen, "Segmenting articular cartilage automatically using a voxel classification approach, " IEEE Trans. Med. Imag., vol. 26, no. 1, pp. 106-115, Jan. 2007.
13. Y. Freund and R. Schapire, "A desicion-theoretic generalization of on-line learning and an application to boosting, " in Computational Learning Theory. New York: Springer, 1995, pp. 23-37.
14. E. Geremia, O. Clatz, B. H. Menze, E. Konukoglu, A. Criminisi, and N. Ayache, "Spatial decision forests for ms lesion segmentation in multichannel magnetic resonance images, " NeuroImage, vol. 57, no. 2, pp. 378-390, 2011.
15. H. Greenspan, A. Ruf, and J. Goldberger, "Constrained Gaussian mixture model framework for automatic segmentation of MR brain images, " IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1233-1245, Sep. 2006.
16. M. A. Ikram, A. van der Lugt, W. J. Niessen, G. P. Krestin, P. J. Koudstaal, A. Hofman, M. M. B. Breteler, and M. W. Vernooij, "The Rotterdam scan study: Design and update up to 2012, " Eur. J. Epidemiol., vol. 26, no. 10, pp. 811-824, 2011.
17. J. Jiang and C. Zhai, "Instance weighting for domain adaptation in NLP, " in Annu. Meet.-Assoc. Computat. Linguist., 2007, vol. 45, p. 264.
18. F. Liu, D. Xu, M. S. Ferguson, B. Chu, T. Saam, N. Takaya, T. S. Hatsukami, C. Yuan, and W. S. Kerwin, "Automated in vivo segmentation of carotid plaque MRI with morphology-enhanced probability maps, " Magn. Reson. Med., vol. 55, no. 3, pp. 659-668, 2006.
19. J. L. Marroquin, B. C. Vemuri, S. Botello, E. Calderon, and A. Fernandez-Bouzas, "An accurate and efficient Bayesian method for automatic segmentation of brain MRI, " IEEE Trans. Med. Imag., vol. 21, no. 8, pp. 934-945, Aug. 2002.
20. A. Mayer and H. Greenspan, "An adaptive mean-shift framework for MRI brain segmentation, " IEEE Trans. Med Imag., vol. 28, no. 8, pp. 1238-1250, Aug. 2009.
21. L. G. Nyul, J. K. Udupa, and X. Zhang, "New variants of a method of MRI scale standardization, " IEEE Trans. Med. Imag., vol. 19, no. 2, pp. 143-150, Feb. 2000.
22. S. J. Pan and Q. Yang, "A survey on transfer learning, " IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
23. Z. Peng, W. Wee, and J. H. Lee, "Automatic segmentation of MR brain images using spatial-varying Gaussian mixture and Markov random field approach, " in Proc. Comput. Vis. Pattern Recognit. Workshop Conf., 2006, p. 80.
24. N. D. Prins, E. J. van Dijk, T. den Heijer, S. E. Vermeer, P. J. Koudstaal, M. Oudkerk, A. Hofman, and M. M. B. Breteler, "Cerebral white matter lesions and the risk of dementia, " Arch. Neurol., vol. 61, no. 10, pp. 1531-1534, 2004.
25. J. C. Rajapakse and F. Kruggel, "Segmentation of MR images with intensity inhomogeneities, " Image Vis. Comput., vol. 16, no. 3, pp. 165-180, 1998.
26. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, M. A: MIT Press, 2001.
27. M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold, D. L. Collins, and T. Arbel, "Evaluating intensity normalization on MRIs of human brain with multiple sclerosis, " Med. Image Anal., vol. 15, no. 2, pp. 267-282, 2011.
28. D. W. Shattuck et al., "Magnetic resonance image tissue classification using a partial volume model, " NeuroImage, vol. 13, no. 5, pp. 856-876, 2001.
29. M. Y. Siyal and L. Yu, "An intelligent modified fuzzy-means based algorithm for bias estimation and segmentation of brain MRI, " Pattern Recognit. Lett., vol. 26, no. 13, pp. 2052-2062, 2005.
30. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data, " IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998.
31. S. M. Smith, "Fast robust automated brain extraction, " Human Brain Mapp., vol. 17, no. 3, pp. 143-155, 2002.
32. M. Styner, J. Lee, B. Chin, M. Chin, O. Commowick, H. Tran, S. Markovic-Plese, V. Jewells, and S. Warfield, "3D segmentation in the clinic: A grand challenge II: MS lesion segmentation, " MIDAS J., pp. 1-5, 2008.
33. J. Tohka et al., "Fast and robust parameter estimation for statistical partial volume models in brain MRI, " Neuroimage, vol. 23, no. 1, pp. 84-97, 2004.
34. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based tissue classification of MR images of the brain, " IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 897-908, Oct. 1999.
35. A. van Opbroek, M. Ikram, M. Vernooij, and M. de Bruijne, "Supervised image segmentation across scanner protocols: A transfer learning approach, " Mach. Learn. Med. Imag., pp. 160-167, 2012.
36. S. E. Vermeer, M. Hollander, E. J. van Dijk, A. Hofman, P. J. Koudstaal, and M. M. B. Breteler, "Silent brain infarcts and white matter lesions increase stroke risk in the general population the Rotterdam scan study, " Stroke, vol. 34, no. 5, pp. 1126-1129, 2003.
37. H. A. Vrooman, C. A. Cocosco, F. van der Lijn, R. Stokking, M. A. Ikram, M. W. Vernooij, M. M. Breteler, and W. J. Niessen, "Multi-spectral brain tissue segmentation using automatically trained k-nearest-neighbor classification, " Neuroimage, vol. 37, no. 1, p. 71, 2007.
38. W. M. Wells, III, W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, "Adaptive segmentation of MRI data, " IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429-442, 1996.
39. M. Wels, Y. Zheng, M. Huber, J. Hornegger, and D. Comaniciu, "A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction, " Phys. Med. Biol., vol. 56, no. 11, p. 3269, 2011.
40. A. J. Worth, The Internet Brain Segmentation Repository (IBSR).
41. P. Wu and T. G. Dietterich, "Improving SVM accuracy by training on auxiliary data sources, " in Proc. 21st Int. Conf. Mach. Learn., 2004, p. 110.
42. J. Yang, R. Yan, and A. G. Hauptmann, "Cross-domain video concept detection using adaptive SVMs, " in Proc. 15th Int. Conf. Multimedia, 2007, pp. 188-197.
43. Z. Yi, A. Criminisi, J. Shotton, and A. Blake, "Discriminative, semantic segmentation of brain tissue in MR images, " in Med. Image Comput. Comput.-Assist. Intervent.-MICCAI 2009, 2009, pp. 558-565.
44. Y. Zhou and J. Bai, "Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI, " IEEE Trans. Biomed. Eng., vol. 54, no. 1, pp. 122-129, 2007.