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Automated detection of multiple sclerosis lesions in serial brain MRI

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Abstract

Introduction

Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided.

Methods

Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge.

Results

This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends.

Conclusion

Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.

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References

  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

    Article  PubMed  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  11. Compston A, Coles A (2006) Multiple sclerosis. Lancet 359(9313):1221–1231

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  13. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    PubMed  CAS  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  22. Hill DLG, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46(3):R1–R45

    Article  PubMed  CAS  Google Scholar 

  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

    Article  Google Scholar 

  24. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  26. Juang LH, Wu MN (2010) MRI brain lesion image detection based on color-converted k-means clustering segmentation. Measurement 43:941–949

    Article  Google Scholar 

  27. Kikinis R, Guttmann CRG, Metcalf D (1999) Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. J Magn Reson Imaging 9:519–530

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  32. Lladó X, Oliver A, Cabezas M, Freixenet J, Vilanova JC, Quiles A, Valls L, Ramió-Torrentà L, Rovira A (2012) Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf Sci 186(1):164–185

    Article  Google Scholar 

  33. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–37

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  35. Meier DS, Guttmann CRG (2003) Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 20:1193–1209

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  38. Moraal B, Meier DS, Poppe PA (2009) Subtraction MR images in a multiple sclerosis multicenter clinical trial setting. Radiology 250:506–514

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

  46. Rovira A, León A (2008) MR in the diagnosis and monitoring of multiple sclerosis: an overview. Eur J Radiol 67(3):409–414

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  52. Solomon J, Sood A (2004) 4-D lesion detection using expextation-maximization and hidden markov model. In: Proc. IEEE Int. Symp. Biomed. Imag., pp 125–128

  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

    Article  PubMed  Google Scholar 

  54. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32(1):71–86

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  56. Tan IL, van Schijndel RA, van Walderveen 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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  63. Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 4:43–55

    Article  PubMed  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  CAS  Google Scholar 

  67. Wells WM III, Grimson WEL (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15:429–443

    Article  PubMed  CAS  Google Scholar 

  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) Spatio-temporal segmentation of active multiple sclerosis lesions in serial MRI data. In: Proc. Int. Conf. Inform. Proc. Medical Imaging, pp 438–445

  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

    Article  PubMed  Google Scholar 

  71. Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Toyofuku F, Ohki M, Higashida Y, Korogi Y (2010) Computer-aided 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

    Article  PubMed  Google Scholar 

  72. Yoon HJ, Zheng B, Sahiner B, Chakraborty DP (2007) Evaluating computer-aided detection algorithms. Med Phys 34(6):2024–2038

    Article  PubMed  Google Scholar 

  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

    Google Scholar 

  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

    Article  PubMed  Google Scholar 

  76. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000

    Article  Google Scholar 

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Acknowledgments

This study has been supported by the Instituto de Salud Carlos III Grant PI09/91018 and Grant VALTEC09-1-0025 from the Generalitat de Catalunya, and by the Fundació Esclerosi Múltiple de Catalunya through the CEM-Cat 2011 Grant Miquel Martí i Pol. O. Ganiler holds a FI grant.

Conflict of interest

We declare that we have no conflict of interest.

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Authors and Affiliations

  1. Computer Vision and Robotics Group, University of Girona, Edifici P-IV Campus de Montilivi s/n, 17071, Girona, Spain

    Xavier Lladó, Onur Ganiler, Arnau Oliver, Robert Martí & Jordi Freixenet

  2. Department of Radiology, Dr. Josep Trueta University Hospital, Girona, Spain

    Laia Valls

  3. Girona Magnetic Resonance Center, Girona, Spain

    Joan C. Vilanova

  4. Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Institut d’Investigació Biomèdica de Girona, Girona, Spain

    Lluís Ramió-Torrentà

  5. Magnetic Resonance Unit, Department of Radiology, Vall d’Hebron University Hospital, Barcelona, Spain

    Àlex Rovira

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  1. Xavier Lladó
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Correspondence to Xavier Lladó.

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Lladó, X., Ganiler, O., Oliver, A. et al. Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 54, 787–807 (2012). https://doi.org/10.1007/s00234-011-0992-6

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