Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI

IEEE Transactions on Medical Imaging

Volumn 32, Issue 8, 2013, Pages 1490-1503

  Elliott, Colm ^a   Arnold, Douglas L ^b   Collins, D Louis ^c   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

^b NEURORX RESEARCH   (Canada)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

Bayesian inference; change detection; machine learning; multiple sclerosis; new lesion segmentation; subtraction imaging

Indexed keywords

BAYESIAN INFERENCE; CHANGE DETECTION; LESION SEGMENTATIONS; MULTIPLE SCLEROSIS; SUBTRACTION IMAGING;

BAYESIAN NETWORKS; COMPUTERIZED TOMOGRAPHY; EXPERIMENTS; INFERENCE ENGINES; LEARNING SYSTEMS;

MAGNETIC RESONANCE IMAGING;

ADULT; ARTICLE; BAYES THEOREM; BRAIN TISSUE; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DISEASE ACTIVITY; DISEASE CLASSIFICATION; FEMALE; FOLLOW UP; HUMAN; MAJOR CLINICAL STUDY; MALE; MULTICENTER STUDY; MULTIPLE SCLEROSIS; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; SENSITIVITY AND SPECIFICITY;

BAYES THEOREM; BRAIN; DATABASES, FACTUAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; REPRODUCIBILITY OF RESULTS;

EID: 84881429677     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2258403     Document Type: Article

References (37)

1. Y. Duan, P. Hildenbrand, M. Sampat, D. Tate, I. Csapo, B. Moraal, R. Bakshi, F. Barkhof, D. Meier, and C. Guttmann, "Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis," Am. J. Neuroradiol., vol. 29, no. 2, pp. 340-346, 2008. (Pubitemid 351287893)
2. B. Moraal et al., "Subtraction MR images in a multiple sclerosis multicenter clinical trial setting1," Radiology, vol. 250, no. 2, pp. 506-514, 2009.
3. A. Evans, J. Frank, J. Antel, and D. Miller, "The role of MRI in clinical trials of multiple sclerosis: Comparison of image processing techniques," Ann. Neurol., vol. 41, no. 1, pp. 125-132, 1997. (Pubitemid 27057105)
4. P. Molyneux, P. Tofts, A. Fletcher, B. Gunn, P. Robinson, H. Gallagher, 1. Moseley, G. Barker, and D. Miller, "Precision and reliability for mea surement of change in MRI lesion volume in multiple sclerosis: A com parison of two computer assisted techniques," J. Neurol., Neurosurg. Psychiatry, vol. 65, no. 1, pp. 42-47, 1998. (Pubitemid 28302355)
5. C. Guttmann et al., "Quantitative follow-up of patients with multiple sclerosis using MRI: Reproducibility," J. Magn. Reson. Imag., vol. 9, no. 4, pp. 509-518, 1999. (Pubitemid 29194288)
6. P. Molyneux, D. Miller, M. Filippi, T. Yousry, E. Radü, H. Ader, and F. Barkhof, "Visual analysis of serial t2-weighted MRI in multiple sclerosis: Intra-and interobserver reproducibility," Neuroradiology, vol. 41, no. 12, pp. 882-888, 1999. (Pubitemid 30000290)
7. E. Ashton, C. Takahashi, M. Berg, A. Goodman, S. Totterman, and S. Ekholm, "Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI," J. Magn. Reson. Imag., vol. 17, no. 3, pp. 300-308, 2003. (Pubitemid 36259243)
8. B. Moraal et al., "Long-interval t2-weighted subtraction magnetic resonance imaging: A powerful new outcome measure in multiple sclerosis trials," Ann. Neurol., vol. 67, no. 5, pp. 667-675, 2010.
9. M. P. Sormani, B. Stubinski, P. Cornelisse, S. Rocak, D. Li, and N. D. Stefano, "Magnetic resonance active lesions as individual-level surrogate for relapses in multiple sclerosis," Multiple Sclerosis J., vol. 17, no. 5, pp. 541-549, 2011.
10. K. Van Leemput, F. Maes, D. Vandermeulen, A. Colchester, and P. Suetens, "Automated segmentation of multiple sclerosis lesions by model outlier detection," IEEE Trans. Med. Imag., vol. 20, no. 8, pp. 677-688, Aug. 2001. (Pubitemid 32813541)
11. X. Wei, S. K. Warfield, K. H. Zou, Y. Wu, X. Li, A. Guimond, J. P. Mugler, R. R. Benson, L. Wolfson, H. L. Weiner, and C. R. Guttmann, "Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy," J. Magn. Reson. Imag., vol. 15, no. 2, pp. 203-209, 2002. (Pubitemid 34112912)
12. M. Bosc, F. Heitz, J.-P. Armspach, I. Namer, D. Gounot, and L. Rumbach, "Automatic change detection in multimodal serial MRI: Application to multiple sclerosis lesion evolution," NeuroImage, vol. 20, no. 2, pp. 643-656, 2003. (Pubitemid 37268094)
13. A. Zijdenbos, R. Forghani, and A. Evans, "Automatic "pipeline" analysis of 3-d MRI data for clinical trials: Application to multiple sclerosis," IEEE Trans. Med. Imag., vol. 21, no. 10, pp. 1280-1291, Oct. 2002.
14. R. Harmouche, L. Collins, D. Arnold, S. Francis, and T. Arbel, "Bayesian MS lesion classification modeling regional and local spatial information," in Proc. 18th Int. Conf. Pattern Recognit., 2006, vol. 3, pp. 984-987, 0-0. (Pubitemid 46553286)
15. D. Garcia-Lorenzo, S. Prima, D. Arnold, D. Collins, and C. Barillot, "Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis," IEEE Trans. Med. Imag., vol. 30, no. 8, pp. 1455-1467, Aug. 2011.
16. S. Francis, "Automatic lesion identification in MRI of multiple sclerosis patients," Ph.D. dissertation, McGill Univ., Montreal, Canada, 2004.
17. X. Lladó, A. Oliver, M. Cabezas, J. Freixenet, J. Vilanova, A. Quiles, L. Valls, L. Ramió-Torrentà, and À. Rovira, "Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches," Inf. Sci., 2011.
18. E. Geremia, O. Clatz, B. 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.
19. J. Thirion and G. Calmon, "Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences," IEEE Trans. Med. Imag., vol. 18, no. 5, pp. 429-441, May 1999.
20. G. Gerig, D. Welti, C. Guttmann, A. Colchester, and G. Székely, "Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data," Med. Image Anal., vol. 4, no. 1, pp. 31-42, 2000.
21. G. Székely, D. Welti, G. Gerig, E. Radü, and L. Kappos, "Spatio-temporal segmentation of active multiple sclerosis lesions in serial mri data," in Information Processing in Medical Imaging. New York: Springer, 2001, pp. 438-445. (Pubitemid 33293300)
22. D. Rey, G. Subsol, H. Delingette, and N. Ayache, "Automatic detection and segmentation of evolving processes in 3d medical images: Application to multiple sclerosis," Med. Image Anal., vol. 6, no. 2, pp. 163-179, 2002. (Pubitemid 35282824)
23. S. Prima, N. Ayache, A. Janke, S. Francis, D. Arnold, and D. Collins, "Statistical analysis of longitudinal MRI data: Applications for detection of disease activity in MS," in Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), 2002, pp. 363-371.
24. L. Aït-Ali, S. Prima, P. Hellier, B. Carsin, G. Edan, and C. Barillot, "Strem: A robust multidimensional parametric method to segment MS lesions in MRI," in Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), 2005, pp. 409-416. (Pubitemid 43828277)
25. M. Lee, S. Smith, J. Palace, and P. Matthews, "Defining multiple sclerosis disease activity using MRI t2-weighted difference imaging," Brain, vol. 121, no. 11, pp. 2095-2102, 1998. (Pubitemid 28497907)
26. I. Tan, R. Van Schijndel, F. Fazekas, M. Filippi, P. Freitag, D. Miller, T. Yousry, P. Pouwels, H. Adèr, and F. Barkhof, "Image registration and subtraction to detect active t 2 lesions in MS: An interobserver study," J. Neurol., vol. 249, no. 6, pp. 767-773, 2002. (Pubitemid 34651760)
27. M. Liguori et al., "One year activity on subtraction MRI predicts subsequent 4 year activity and progression in multiple sclerosis," J. Neurol., Neurosurg. Psychiatry, vol. 82, no. 10, pp. 1125-1131, 2011.
28. C. Elliott, S. Francis, D. Arnold, D. Collins, and T. Arbel, "Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images," in Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), 2010, pp. 290-297.
29. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001. (Pubitemid 32933532)
30. V. Fonov et al., "Unbiased average age-appropriate atlases for pediatric studies," NeuroImage, vol. 54, no. 1, pp. 313-327, 2011.
31. S. Smith, "Fast robust automated brain extraction," Human Brain Mapp., vol. 17, no. 3, pp. 143-155, 2002. (Pubitemid 35253488)
32. J. Sled, A. Zijdenbos, and A. 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. (Pubitemid 128742012)
33. D. Collins, C. Holmes, T. Peters, and A. Evans, "Automatic 3-d modelbased neuroanatomical segmentation," Human Brain Mapp., vol. 3, no. 3, pp. 190-208, 1995. (Pubitemid 3036541)
34. L. Nyul, J. 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. (Pubitemid 30594807)
35. P. Anbeek, K. L. Vincken, M. J. van Osch, R. H. Bisschops, and J. van der Grond, "Probabilistic segmentation of white matter lesions in MR imaging," NeuroImage, vol. 21, no. 3, pp. 1037-1044, 2004. (Pubitemid 38314966)
36. A. Akselrod-Ballin, M. Galun, R. Basri, A. Brandt, M. Gomori, M. Fil-ippi, and P. Valsasina, "An integrated segmentation and classification approach applied to multiple sclerosis analysis," in IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2006, vol. 1, pp. 1122-1129. (Pubitemid 44931447)
37. N. Shiee, P.-L. Bazin, A. Ozturk, D. S. Reich, P. A. Calabresi, and D. L. Pham, "A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions," NeuroImage, vol. 49, no. 2, pp. 1524-1535, 2010.