Automated detection of multiple sclerosis lesions in serial brain MRI

Neuroradiology

Volumn 54, Issue 8, 2012, Pages 787-807

  Lladó, Xavier ^a   Ganiler, Onur ^a   Oliver, Arnau ^a   Marti, Robert ^a   Freixenet, Jordi ^a   Valls, Laia ^b   Vilanova, Joan C ^c   Ramió Torrentà, Lluis ^d   Rovira, Alex ^e  

^a UNIVERSITY OF GIRONA   (Spain)

^b HOSPITAL JOSEP TRUETA   (Spain)

^c Girona Magnetic Resonance Center   (Spain)

^d GIRONA BIOMEDICAL RESEARCH INSTITUTE IDIBGI   (Spain)

^e HOSPITAL UNIVERSITARI VALL D HEBRON   (Spain)

Author keywords

MRI; Multiple sclerosis; Review; Serial analysis

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATION; CEREBROSPINAL FLUID; CLASSIFIER; DISEASE ACTIVITY; DISEASE CLASSIFICATION; FOLLOW UP; GRAY MATTER; HUMAN; MULTIPLE SCLEROSIS; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; TIME SERIES ANALYSIS; WHITE MATTER LESION;

BRAIN; CONTRAST MEDIA; DISEASE PROGRESSION; HUMANS; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED;

EID: 84866029179     PISSN: 00283940     EISSN: 14321920     Source Type: Journal    
DOI: 10.1007/s00234-011-0992-6     Document Type: Article

References (76)

1. Anbeek P, Vincken KL, van Osch MJ (2004) Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 8:205-215 (Pubitemid 39185905)
2. Antel SB, Collins DL, Bernasconi N, Andermann F, Singhal R, Kearney RE, Arnold D, Bernasconi A (2003) Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis. IEEE Trans Med Imaging 19(4):1748-1759 (Pubitemid 37025870)
3. Arimura H, Magome T, Yamashita Y, Yamamoto D (2009) Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2(3):925-952
4. Ashton EA, Takahashi C, Berg MJ, Goodman A, Totterman S, Ekholm S (2003) Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI. J Magn Reson Imaging 17:300-308 (Pubitemid 36259243)
5. Bosc M, Heitz F, Armspach J, Namer I, Gounot D, Rumbach L (2003) Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution. NeuroImage 20 (2):643-656 (Pubitemid 37268094)
6. Cabezas M, Oliver A, Lladó X, Freixenet J, Bach-Cuadra M (2011) A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed 104(3): e158-e177
7. Calcagno G, Staiano A, Fortunato G, Brescia-Morra V, Salvatore E, Liguori R, Capone S, Filla A, Longo G, Sacchetti L (2010) A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients. Inf Sci 180(21):4153-4163
8. Brain Imaging Center M (2010) BrainWeb: simulated brain database. http://mouldy.bic.mni.mcgill.ca/brainweb/, last visit: 29/10/2011
9. Cerasa A, Bilotta E, Augimeri A, Cherubini A, Pantano P, Zito G, Lanza P, Valentino P, Gioia MC, Quattrone A (2011) A cellular neural network methodology for the automated segmentation of multiple sclerosis lesions. J Neurosci Methods In press
10. Chakraborty DP, Yoon HJ, Mello-Thomas C (2007) Localization accuracy of radiologists in free-response studies: inferring perceptual FROC curves from mark-rating data. Med Phys 14(1):4-18
11. Compston A, Coles A (2006) Multiple sclerosis. Lancet 359 (9313):1221-1231
12. Curati WL, Williams EJ, Oatridge A, Hajnal JV, Saeed N, Bydder GM (1996) Use of subvoxel registration and subtraction to improve demonstration of contrast enhancement in MRI of the brain. Neuroradiology 38:717-723 (Pubitemid 26390301)
13. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297-302
14. Doi K (2006) Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology. Phys Med Biol 51(13):R5-R27 (Pubitemid 43958605)
15. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4-5):198-211 (Pubitemid 46601790)
16. Duan Y, Hildenbrand PG, Sampat MP (2008) Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis. Am J Neuroradiol 29:340-346 (Pubitemid 351287893)
17. Ettinger GJ, Grimson WEL, Lozano-Perez T, III WMW, White SJ, Kikinis R (1994) Automatic registration for multiple sclerosis change detection. Int Proc. IEEE Work. Biomed. Image Anal., pp 297-306
18. Ge Y (2006) Multiple sclerosis: the role of MR imaging. Am J Neuroradiol 27(6):1165-1176 (Pubitemid 44897620)
19. Gerig G, Martin J, Kikinis R, Kübler O, Shenton M, Jolesz FA (1992) Unsupervised segmentation of 3-D dual-echo MR head data. Image Vis Comput 10:349-360
20. Gerig G, Welti D, Guttmann CRG, Colchester ACF, Székely G (2000) Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Med Image Anal 4:31-42
21. Guttmann CRG, Kikinis R, Anderson MC, Jakab M, Warfield SK, Killiany RJ, Weiner HL, Jolesz FA (1999) Quantitative follow-up of patients with multiple sclerosis using MRI: reproducibility. J Magn Reson Imaging 9:509-518 (Pubitemid 29194288)
22. Hill DLG, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46(3):R1-R45 (Pubitemid 32218849)
23. Hillary FG, Biswal BB (2009) Automated detection and quantification of brain lesions in acute traumatic brain injury using MRI. Brain Imaging Behav 3:111-112
24. JenkinsonM, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143-156
25. Jenkinson M, Bannister PR, Brady JM, Smith S (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825-841 (Pubitemid 35317016)
26. Juang LH, Wu MN (2010) MRI brain lesion image detection based on color-converted k-means clustering segmentation. Measurement 43:941-949
27. Kikinis R, Guttmann CRG, Metcalf D (1999) Quantitative followup of patients with multiple sclerosis using MRI: technical aspects. J Magn Reson Imaging 9:519-530 (Pubitemid 29194289)
28. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3):786-802
29. Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan RN, Davatzikos C (2008) Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Acad Radiol 15(3):300-313 (Pubitemid 351246730)
30. Lee MA, Smith S, Palace J, Matthews PM (1998) Defining multiple sclerosis disease activity using MRI T2-weighted difference imaging. Brain 121:2095-2102 (Pubitemid 28497907)
31. Lemieux L, Wieshmann U, Moran N, Fish D, Shorvon S (1998) The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization. Med Image Anal 2(3):227-242 (Pubitemid 128499388)
32. Lladó X, Oliver A, Cabezas M, Freixenet J, Vilanova JC, Quiles A, Valls L, Ramió-TorrentàL, Rovira A (2012) Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf Sci 186(1):164-185
33. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1-37
34. Martola J, Bergström J, Fredrikson S, Stawiarz L, Hillert J, Zhang Y, Flodmark O, Lilja A, Ekbom A, Aspelin P, Wiberg MK (2010) A longitudinal observational study of brain atrophy rate reflecting four decades of multiple sclerosis: a comparison of serial 1D, 2D, and volumetric measurements from MRI images. Neuroradiology 52(2):109-117
35. Meier DS, Guttmann CRG (2003) Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 20:1193-1209 (Pubitemid 37268137)
36. Metcalf D, Kikinis R, Guttmann CRG, Vaina L, Jolesz F (1988) 4D connected component labelling applied to quantitative analysis of MS lesion temporal development. In: Proc. IEEE Eng. Med. Biol. Society, pp 945-946
37. Molyneux P, Tofts P, Fletcher A, Gunn B, Robinson P, Gallagher H, Moseley I, Barker G, Miller D (1998) Precision and reliability for measurement of change in MRI lesion volume in multiple sclerosis: a comparison of two computer assisted techniques. J Neurol Neurosurg Psychiatry 65:42-47 (Pubitemid 28302355)
38. Moraal B, Meier DS, Poppe PA (2009) Subtraction MR images in a multiple sclerosis multicenter clinical trial setting. Radiology 250:506-514
39. Moraal B, Wattjes MP, Geurts JJG (2010) Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology 255(1)
40. Mortazavi D, Kouzani AZ, Soltanian-Zadeh H (2011) Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology (in press)
41. Patriarche J, Erickson B (2004) A review of the automated detection of change in serial imaging studies of the brain. J Digit Imaging 17(3):158-174 (Pubitemid 40378989)
42. Pieperhoff P, Sudmeyer M, Homke L, Zilles K, Schnitzler A, Amunts K (2008) Detection of structural changes of the human brain in longitudinally acquired MR images by deformation field morphometry: methodological analysis, validation and application. NeuroImage 43(2):269-287
43. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Wilde MH, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292-302
44. Rey D, Subsol G, Delingette H (2002) Automatic detection and segmentation of evolving processes in 3D medical images: applications to multiple sclerosis. Med Image Anal 6:163-179 (Pubitemid 35282824)
45. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., pp 1115-1124 (Pubitemid 128152762)
46. Rovira A, León A (2008) MR in the diagnosis and monitoring of multiple sclerosis: an overview. Eur J Radiol 67(3):409-414
47. Rovira A, Swanton J, Tintor M, Huerga E, Barkhof F, Filippi M, Frederiksen JL, Langkilde A, Miszkiel K, Polman C, Rovaris M, Sastre-Garriga J, Miller D, Montalban X (2009) A single, early magnetic resonance imaging study in the diagnosis of multiple sclerosis. Arch Neurol 66(5):587-592
48. Shah M, Xiao Y, Subbanna N, Francis S, Arnold DL, Collins DL, Arbel T (2011) Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Med Image Anal 15 (2):267-282
49. Shen S, Szameitat A, Sterr A (2008) Detection of infarct lesions from brain MRI images using inconsistency between voxel intensity and spatial location. A 3D automatic approach. IEEE Trans Inf Technol Biomed 12(4):532-540
50. Shiee N, Bazin PL, Ozturk A, Reich DS, Calabresi PA, Pham DL (2010) A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage 49 (2):1524-1535
51. Smith S, Zhang Y, Jenkinson M, Chen J, Matthews P, Federico A, Stefano ND (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage 17(1):479- 489
52. Solomon J, Sood A (2004) 4-D lesion detection using expextationmaximization and hidden markov model. In: Proc. IEEE Int. Symp. Biomed. Imag., pp 125-128 (Pubitemid 40513891)
53. Srivastava S, Maes F, Vandermeulen D, Paesschen WV, Dupont P, Suetens P (2005) Automatic detection of focal cortical dysplastic lesions. NeuroImage 27:253-266
54. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32(1):71-86
55. Tan IL, van Schijndel RA, Fazekas F, Filippi M, Freitag P, Miller DH, Yousry TA, Pouwels PJW, Adèr HJ, Barkhof F (2002) Image registration and subtraction to detect active T2 lesions in MS: an interobserver study. J Neurol 249(5):767-773 (Pubitemid 34651760)
56. Tan IL, van Schijndel RA, vanWalderveen MAA, Quist M, Bos R, Pouwels PJW, Desmedt P, Adèr HJ, Barkhof F (2002) Magnetic resonance image registration in multiple sclerosis: comparison with repositioning error and observer-based variability. J Magn Reson Imaging 15(5):505-510 (Pubitemid 34461544)
57. Thirion JP, Calmon G (1999) Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences. IEEE Trans Med Imaging 18:429-441
58. Tian W, Zhu T, Zhong J, Liu X, Rao P, Segal BM, Ekholm S (2011) Progressive decline in fractional anisotropy on serial DTI examinations of the corpus callosum: a putative marker of disease activity and progression in SPMS. Neuroradiology (in press)
59. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4itk: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310-1320
60. Udupa J, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graph Model Image Proc 58(3):246-261 (Pubitemid 126381825)
61. Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI (1997) Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Trans Med Imaging 16 (5):598-609 (Pubitemid 127767638)
62. van den Elskamp IJ, Boden B, Dattola V, Knol DL, Filippi M, Kappos L, Fazekas F, Wagner K, Pohl C, Sandbrink R, Polman CH, Uitdehaag BMJ, Barkhof F (2010) Cerebral atrophy as outcome measure in short-term phase 2 clinical trials in multiple sclerosis. Neuroradiology 52(10):875-881
63. Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 4:43-55
64. Warfield SK, Zou KH, Wells WM III (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23 (7):903-921
65. Wei X, Warfield SK, Zou KH, Wu Y, Li X, Guimond A, Mugler JP III, Benson RR, Wolfson L, Weiner HL, Guttmann CRG (2002) Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J Magn Reson Imaging 15:203-209 (Pubitemid 34112912)
66. Weiner HL, Guttmann CRG, Khoury SJ, Orav EJ, Hohol MJ, Kikinis R, Jolesz FA (2000) Serial magnetic resonance imaging in multiple sclerosis: correlation with attacks, disability, and disease stage. J Neuroimmunol 104:164-173 (Pubitemid 30125407)
67. Wells WM III, Grimson WEL (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15:429-443
68. Wells III WM, Grimson WEL, Kikinis R, Jolesz FA (1994) Statistical intensity correction and segmentation of MRI data. In: Proc. SPIE Conf. Visualization Biomed. Computing, pp 13-24
69. Welti D, Gerig G, Radü EW, Kappos L, Székely G (2001) Spatiotemporal segmentation of active multiple sclerosis lesions in serial MRI data. In: Proc. Int. Conf. Inform. Proc. Medical Imaging, pp 438-445 (Pubitemid 33293300)
70. Wu Y, Warfield SK, Tan IL, Wells WM III, Meier DS, van Schijndel RA, Barkhof F, Guttmann C (2006) Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. NeuroImage 32(3):1205-1215 (Pubitemid 44294773)
71. Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Toyofuku F, Ohki M, Higashida Y, Korogi Y (2010) Computeraided detection of multiple sclerosis lesions in brain magnetic resonance images: false positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph 34(5):404-413
72. Yoon HJ, Zheng B, Sahiner B, Chakraborty DP (2007) Evaluating computer-aided detection algorithms. Med Phys 34(6):2024-2038 (Pubitemid 47055738)
73. Zacharaki EI, Kanterakis S, Bryan RN, Davatzikos C (2008) Measuring brain lesion progression with a supervised tissue classification system. Proc Int Conf Med Image Comput Comput Assist Interv 11:620-627
74. Zar J (1984) Measures of dispersion and variability. In: Zar J (ed) Biostatistical analysis, Prentice Hall, Englewood Cliffs, NJ. pp 27- 39
75. Zijdenbos AP, Forghani R, Evans AC (2002) Automatic pipeline analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21(10):1280- 1291
76. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977-1000