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Magnetic Resonance Materials in Physics, Biology and Medicine

Published in:

01-04-2016 | Review Article

Segmentation of the human spinal cord

Authors: Benjamin De Leener, Manuel Taso, Julien Cohen-Adad, Virginie Callot

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 2/2016

Abstract

Segmenting the spinal cord contour is a necessary step for quantifying spinal cord atrophy in various diseases. Delineating gray matter (GM) and white matter (WM) is also useful for quantifying GM atrophy or for extracting multiparametric MRI metrics into specific WM tracts. Spinal cord segmentation in clinical research is not as developed as brain segmentation, however with the substantial improvement of MR sequences adapted to spinal cord MR investigations, the field of spinal cord MR segmentation has advanced greatly within the last decade. Segmentation techniques with variable accuracy and degree of complexity have been developed and reported in the literature. In this paper, we review some of the existing methods for cord and WM/GM segmentation, including intensity-based, surface-based, and image-based methods. We also provide recommendations for validating spinal cord segmentation techniques, as it is important to understand the intrinsic characteristics of the methods and to evaluate their performance and limitations. Lastly, we illustrate some applications in the healthy and pathological spinal cord. One conclusion of this review is that robust and automatic segmentation is clinically relevant, as it would allow for longitudinal and group studies free from user bias as well as reproducible multicentric studies in large populations, thereby helping to further our understanding of the spinal cord pathophysiology and to develop new criteria for early detection of subclinical evolution for prognosis prediction and for patient management. Another conclusion is that at the present time, no single method adequately segments the cord and its substructure in all the cases encountered (abnormal intensities, loss of contrast, deformation of the cord, etc.). A combination of different approaches is thus advised for future developments, along with the introduction of probabilistic shape models. Maturation of standardized frameworks, multiplatform availability, inclusion in large suite and data sharing would also ultimately benefit to the community.

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Stroman PW, Wheeler-Kingshott C, Bacon M, Schwab JM, Bosma R, Brooks J, Cadotte D, Carlstedt T, Ciccarelli O, Cohen-Adad J, Curt A, Evangelou N, Fehlings MG, Filippi M, Kelley BJ, Kollias S, Mackay A, Porro CA, Smith S, Strittmatter SM, Summers P, Tracey I (2014) The current state-of-the-art of spinal cord imaging: methods. Neuroimage 84:1070–1081PubMedPubMedCentralCrossRef

Wheeler-Kingshott CA, Stroman PW, Schwab JM, Bacon M, Bosma R, Brooks J, Cadotte DW, Carlstedt T, Ciccarelli O, Cohen-Adad J, Curt A, Evangelou N, Fehlings MG, Filippi M, Kelley BJ, Kollias S, Mackay A, Porro CA, Smith S, Strittmatter SM, Summers P, Thompson AJ, Tracey I (2014) The current state-of-the-art of spinal cord imaging: applications. Neuroimage 84:1082–1093PubMedPubMedCentralCrossRef

Losseff NA, Webb SL, O’Riordan JI, Page R, Wang L, Barker GJ, Tofts PS, McDonald WI, Miller DH, Thompson AJ (1996) Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. Brain 119(3):701–708PubMedCrossRef

Despotovic I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015:450341PubMedPubMedCentralCrossRef

Fujimoto K, Polimeni JR, van der Kouwe AJ, Reuter M, Kober T, Benner T, Fischl B, Wald LL (2014) Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T. Neuroimage 90:60–73PubMedPubMedCentralCrossRef

Smith SA, Edden RA, Farrell JA, Barker PB, Van Zijl P (2008) Measurement of T1 and T2 in the cervical spinal cord at 3 Tesla. Magn Reson Med 60(1):213–219PubMedPubMedCentralCrossRef

Peters AM, Brookes MJ, Hoogenraad FG, Gowland PA, Francis ST, Morris PG, Bowtell R (2007) T2* measurements in human brain at 1.5, 3 and 7 T. Magn Reson Imaging 25(6):748–753PubMedCrossRef

Kearney H, Yiannakas MC, Abdel-Aziz K, Wheeler-Kingshott CA, Altmann DR, Ciccarelli O, Miller DH (2014) Improved MRI quantification of spinal cord atrophy in multiple sclerosis. J Magn Reson Imaging 39(3):617–623PubMedCrossRef

Papinutto N, Schlaeger R, Panara V, Zhu AH, Caverzasi E, Stern WA, Hauser SL, Henry RG (2015) Age, gender and normalization covariates for spinal cord gray matter and total cross-sectional areas at cervical and thoracic levels: a 2D phase sensitive inversion recovery imaging study. PLoS One 10(3):e0118576PubMedPubMedCentralCrossRef

10.

Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49(2):1271–1281PubMedCrossRef

11.

Held P, Dorenbeck U, Seitz J, Fründ R, Albrich H (2003) MRI of the abnormal cervical spinal cord using 2D spoiled gradient echo multiecho sequence (MEDIC) with magnetization transfer saturation pulse. A T2*-weighted feasibility study. J Neuroradiol 30(2):83–90PubMed

12.

Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97PubMedCrossRef

13.

Buades A, Coll B, Morel J-M (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530CrossRef

14.

Aspert N, Santa Cruz D, Ebrahimi T (2002) MESH: measuring errors between surfaces using the Hausdorff distance. Proceedings of the 2002 IEEE International Conference on Multimedia and Expo. ICME, Lausanne, pp 705–708

15.

Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23(7):903–921PubMedPubMedCentralCrossRef

16.

Tench CR, Morgan PS, Constantinescu CS (2005) Measurement of cervical spinal cord cross-sectional area by MRI using edge detection and partial volume correction. J Magn Reson Imaging 21(3):197–203PubMedCrossRef

17.

El Mendili M-M, Chen R, Tiret B, Villard N, Trunet S, Pélégrini-Issac M, Lehéricy S, Pradat P-F, Benali H (2015) Fast and accurate semi-automated segmentation method of spinal cord MR images at 3T applied to the construction of a cervical spinal cord template. PLoS One 10(3):e0122224PubMedPubMedCentralCrossRef

18.

Behrens T, Rohr K, Stiehl HS (2003) Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking. IEEE Trans Syst Man Cybern B Cybern 33(4):554–561PubMedCrossRef

19.

Zivadinov R, Banas AC, Yella V, Abdelrahman N, Weinstock-Guttman B, Dwyer MG (2008) Comparison of three different methods for measurement of cervical cord atrophy in multiple sclerosis. AJNR Am J Neuroradiol 29(2):319–325PubMedCrossRef

20.

Coulon O, Hickman SJ, Parker GJ, Barker GJ, Miller DH, Arridge SR (2002) Quantification of spinal cord atrophy from magnetic resonance images via a B-spline active surface model. Magn Reson Med 47(6):1176–1185PubMedCrossRef

21.

Horsfield MA, Sala S, Neema M, Absinta M, Bakshi A, Sormani MP, Rocca MA, Bakshi R, Filippi M (2010) Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage 50(2):446–455PubMedPubMedCentralCrossRef

22.

McIntosh C, Hamarneh G (2006) Spinal crawlers: deformable organisms for spinal cord segmentation and analysis. In: Larsen R, Nielsen M, Sporring J (eds) Medical image computing and computer-assisted intervention—MICCAI 2006, vol 4190. Lecture notes in computer science, Springer, Berlin, pp 808–815CrossRef

23.

McIntosh C, Hamarneh G, Toom M, Tam RC (2011) Spinal cord segmentation for volume estimation in healthy and multiple sclerosis subjects using crawlers and minimal paths. In: Proceedings of the First IEEE international conference on healthcare informatics, imaging and systems biology, HISB, San Jose, CA, IEEE, pp 25–31

24.

De Leener B, Kadoury S, Cohen-Adad J (2014) Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 98:528–536PubMedCrossRef

25.

De Leener B, Cohen-Adad J, Kadoury S (2015) Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Trans Med Imaging 34(8):1705–1718PubMedCrossRef

26.

Ullmann E, Paquette JFP, Thong WE, Cohen-Adad J (2014) Automatic labeling of vertebral levels using a robust template-based approach. Int J Biomed Imaging 2014:719520PubMedPubMedCentralCrossRef

27.

Koh J, Kim T, Chaudhary V, Dhillon G (2010) Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field. In: Proceedings of the 2010 annual international conference of the IEEE engineering in medicine and biology society (EMBC 2010), Buenos Aires, IEEE, pp 3117–3120

28.

Koh J, Scott PD, Chaudhary V, Dhillon G (2011) An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model. In: Proceedings of the 8th IEEE international symposium on biomedical imaging: from nano to macro, ISBI, Chicago, IL. pp 1467–1471

29.

Van Uitert R, Bitter I, Butman JA (2005) Semi-automatic spinal cord segmentation and quantification. In: Proceedings of the 19th international congress and exhibition, computer assisted radiology and surgery, Berlin. pp 224–229

30.

Sonkova P, Evangelou IE, Gallo A, Cantor FK, Ohayon J, McFarland HF, Bagnato F (2008) Semi-automatic segmentation and modeling of the cervical spinal cord for volume quantification in multiple sclerosis patients from magnetic resonance images. In: Proceedings of SPIE 6914, medical imaging 2008: image processing. International Society for Optics and Photonics, San Diego, CA, p 69144I

31.

Kawahara J, McIntosh C, Tam R, Hamarneh G (2013) Globally optimal spinal cord segmentation using a minimal path in high dimensions. In: Proceedings of the 10th international symposium on biomedical imaging, ISBI, San Francisco, CA. pp 848–851

32.

Kawahara J, McIntosh C, Tam R, Hamarneh G (2013) Augmenting auto-context with global geometric features for spinal cord segmentation. In: Proceedings of the 4th international workshop on machine learning in medical imaging, Nagoya, Japan. pp 211–218

33.

Law MW, Garvin GJ, Tummala S, Tay K, Leung AE, Li S (2013) Gradient competition anisotropy for centerline extraction and segmentation of spinal cords. In: Proceedings of the 23rd international conference on information processing in medical imaging, Asilomar, CA, pp 49–61

34.

Carbonell-Caballero J, Manjon JV, Marti-Bonmati L, Olalla JR, Casanova B, de la Iglesia-Vaya M, Coret F, Robles M (2006) Accurate quantification methods to evaluate cervical cord atrophy in multiple sclerosis patients. Magn Reson Mater Phy 19(5):237–246CrossRef

35.

Bergo FPG, Franca MC, Chevis CF, Cendes F (2012) SpineSeg: a segmentation and measurement tool for evaluation of spinal cord atrophy. In: Proceedings of the 7th Iberian conference on information systems and technologies, CISTI, Madrid, pp 1–4

36.

Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239CrossRef

37.

Kayal N (2013) An investigation of grow cut algorithm for segmentation of MRI spinal cord images in normals and patients with SCI. Temple University Graduate School, Ann Arbor

38.

Fonov VS, Le Troter A, Taso M, De Leener B, Leveque G, Benhamou M, Sdika M, Benali H, Pradat PF, Collins DL, Callot V, Cohen-Adad J (2014) Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template. Neuroimage 102(Pt 2):817–827PubMedCrossRef

39.

Pezold S, Amann M, Weier K, Fundana K, Radue EW, Sprenger T, Cattin PC (2014) A semi-automatic method for the quantification of spinal cord atrophy. In: Proceedings of the workshop held at the 16th international conference on medical image computing and computer assisted intervention, Nagoya, Japan, pp 143–155

40.

Stroman PW, Figley CR, Cahill CM (2008) Spatial normalization, bulk motion correction and coregistration for functional magnetic resonance imaging of the human cervical spinal cord and brainstem. Magn Reson Imaging 26(6):809–814PubMedCrossRef

41.

Yen C, Su H-R, Lai S-H, Liu K-C, Lee R-R (2013) 3D Spinal cord and nerves segmentation from STIR-MRI. In: Proceedings of the international computer symposium ICS 2012, Hualien, Taiwan, pp 383–392

42.

Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783PubMedCrossRef

43.

Chen M, Carass A, Oh J, Nair G, Pham DL, Reich DS, Prince JL (2013) Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view. Neuroimage 83:1051–1062PubMedCrossRef

44.

Weiler F, Daams M, Lukas C, Barkhof F, Hahn HK (2015) Highly accurate volumetry of the spinal cord. In: Proceedings of SPIE 9413, medical imaging 2015: image processing, Orlando, Florida, p 941302

45.

Pezold S, Fundana K, Amann M, Andelova M, Pfister A, Sprenger T, Cattin P (2015) Automatic segmentation of the spinal cord using continuous max flow with cross-sectional similarity prior and tubularity features. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, vol 20. Lecture Notes in Computational Vision and Biomechanics. Springer International Publishing, pp 107–118

46.

Fradet L, Arnoux PJ, Ranjeva JP, Petit Y, Callot V (2014) Morphometrics of the entire human spinal cord and spinal canal measured from in vivo high-resolution anatomical magnetic resonance imaging. Spine 39(4):E262–E269 (Phila Pa 1976) PubMedCrossRef

47.

Held P, Seitz J, Frund R, Nitz W, Lenhart M, Geissler A (2001) Comparison of two-dimensional gradient echo, turbo spin echo and two-dimensional turbo gradient spin echo sequences in MRI of the cervical spinal cord anatomy. Eur J Radiol 38(1):64–71PubMedCrossRef

48.

Samson R, Ciccarelli O, Kachramanoglou C, Brightman L, Lutti A, Thomas D, Weiskopf N, Wheeler-Kingshott C (2013) Tissue-and column-specific measurements from multi-parameter mapping of the human cervical spinal cord at 3 T. NMR Biomed 26(12):1823–1830PubMedPubMedCentralCrossRef

49.

Ellingson BM, Ulmer JL, Schmit BD (2007) Gray and white matter delineation in the human spinal cord using diffusion tensor imaging and fuzzy logic. Acad Radiol 14(7):847–858PubMedCrossRef

50.

Ellingson BM, Ulmer JL, Schmit BD (2008) Morphology and morphometry of human chronic spinal cord injury using diffusion tensor imaging and Fuzzy logic. Ann Biomed Eng 36(2):224–236PubMedCrossRef

51.

Yiannakas MC, Kearney H, Samson RS, Chard DT, Ciccarelli O, Miller DH, Wheeler-Kingshott CA (2012) Feasibility of grey matter and white matter segmentation of the upper cervical cord in vivo: a pilot study with application to magnetisation transfer measurements. Neuroimage 63(3):1054–1059PubMedCrossRef

52.

Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRef

53.

Tang L, Wen Y, Zhou Z, von Deneen KM, Huang D, Ma L (2013) Reduced field-of-view DTI segmentation of cervical spine tissue. Magn Reson Imaging 31(9):1507–1514PubMedCrossRef

54.

Asman AJ, Bryan FW, Smith SA, Reich DS, Landman BA (2014) Groupwise multi-atlas segmentation of the spinal cord’s internal structure. Med Image Anal 18(3):460–471PubMedPubMedCentralCrossRef

55.

De Leener B, Roux A, Taso M, Callot V, Cohen-Adad J (2015) Spinal cord gray and white matter segmentation using atlas deformation. In: Proceedings of the 23th Annual Meeting of ISMRM, Toronto, Canada, Toronto, p 4424

56.

Taso M, Le Troter A, Sdika M, Cohen-Adad J, Arnoux PJ, Guye M, Ranjeva JP, Callot V (2015) A reliable spatially normalized template of the human spinal cord—applications to automated white matter/gray matter segmentation and tensor-based morphometry (TBM) mapping of gray matter alterations occurring with age. Neuroimage 117:20–28PubMedCrossRef

57.

Taso M, Le Troter A, Sdika M, Ranjeva JP, Guye M, Bernard M, Callot V (2014) Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magn Reson Mater Phy 27(3):257–267CrossRef

58.

Cohen-Adad J, Zhao W, Keil B, Ratai EM, Triantafyllou C, Lawson R, Dheel C, Wald LL, Rosen BR, Cudkowicz M (2013) 7-T MRI of the spinal cord can detect lateral corticospinal tract abnormality in amyotrophic lateral sclerosis. Muscle Nerve 47(5):760–762PubMedCrossRef

59.

Sigmund E, Suero G, Hu C, McGorty K, Sodickson D, Wiggins G, Helpern J (2012) High-resolution human cervical spinal cord imaging at 7 T. NMR Biomed 25(7):891–899PubMedPubMedCentralCrossRef

60.

Lundell H, Barthelemy D, Skimminge A, Dyrby T, Biering-Sørensen F, Nielsen JB (2011) Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury. Spinal Cord 49(1):70–75PubMedCrossRef

61.

Klein JP, Arora A, Neema M, Healy BC, Tauhid S, Goldberg-Zimring D, Chavarro-Nieto C, Stankiewicz JM, Cohen AB, Buckle GJ, Houtchens MK, Ceccarelli A, Dell’Oglio E, Guttmann CR, Alsop DC, Hackney DB, Bakshi R (2011) A 3T MR imaging investigation of the topography of whole spinal cord atrophy in multiple sclerosis. AJNR Am J Neuroradiol 32(6):1138–1142PubMedPubMedCentralCrossRef

62.

Kameyama T, Hashizume Y, Sobue G (1996) Morphologic features of the normal human cadaveric spinal cord. Spine 21(11):1285–1290PubMedCrossRef

63.

Hickman S, Hadjiprocopis A, Coulon O, Miller D, Barker G (2004) Cervical spinal cord MTR histogram analysis in multiple sclerosis using a 3D acquisition and a B-spline active surface segmentation technique. Magn Reson Imaging 22(6):891–895PubMedCrossRef

64.

Ciccarelli O, Wheeler-Kingshott C, McLean M, Cercignani M, Wimpey K, Miller D, Thompson A (2007) Spinal cord spectroscopy and diffusion-based tractography to assess acute disability in multiple sclerosis. Brain 130(8):2220–2231PubMedCrossRef

65.

Cohen-Adad J, Descoteaux M, Rossignol S, Hoge RD, Deriche R, Benali H (2008) Detection of multiple pathways in the spinal cord using q-ball imaging. Neuroimage 42(2):739–749PubMedCrossRef

66.

Gullapalli J, Krejza J, Schwartz ED (2006) In vivo DTI evaluation of white matter tracts in rat spinal cord. J Magn Reson Imaging 24(1):231–234PubMedCrossRef

67.

Klawiter EC, Schmidt RE, Trinkaus K, Liang H-F, Budde MD, Naismith RT, Song S-K, Cross AH, Benzinger TL (2011) Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage 55(4):1454–1460PubMedPubMedCentralCrossRef

68.

Lindberg PG, Feydy A, Maier MA (2010) White matter organization in cervical spinal cord relates differently to age and control of grip force in healthy subjects. J Neurosci 30(11):4102–4109PubMedCrossRef

69.

Narayana PA, Grill RJ, Chacko T, Vang R (2004) Endogenous recovery of injured spinal cord: longitudinal in vivo magnetic resonance imaging. J Neurosci Res 78(5):749–759PubMedCrossRef

70.

Onu M, Gervai P, Cohen-Adad J, Lawrence J, Kornelsen J, Tomanek B, Sboto-Frankenstein UN (2010) Human cervical spinal cord funiculi: investigation with magnetic resonance diffusion tensor imaging. J Magn Reson Imaging 31(4):829–837PubMedCrossRef

71.

Qian W, Chan Q, Mak H, Zhang Z, Anthony MP, Yau KKW, Khong PL, Chan KH, Kim M (2011) Quantitative assessment of the cervical spinal cord damage in neuromyelitis optica using diffusion tensor imaging at 3 Tesla. J Magn Reson Imaging 33(6):1312–1320PubMedCrossRef

72.

Smith SA, Jones CK, Gifford A, Belegu V, Chodkowski B, Farrell JA, Landman BA, Reich DS, Calabresi PA, McDonald JW (2010) Reproducibility of tract-specific magnetization transfer and diffusion tensor imaging in the cervical spinal cord at 3 Tesla. NMR Biomed 23(2):207–217PubMedPubMedCentral

73.

Xu J, Shimony JS, Klawiter EC, Snyder AZ, Trinkaus K, Naismith RT, Benzinger TL, Cross AH, Song SK (2013) Improved in vivo diffusion tensor imaging of human cervical spinal cord. Neuroimage 67:64–76PubMedPubMedCentralCrossRef

74.

Lévy S, Benhamou M, Naaman C, Rainville P, Callot V, Cohen-Adad J (2015) White matter atlas of the human spinal cord with estimation of partial volume effect. NeuroImage 119:262–271PubMedCrossRef

75.

Taso M, Girard O, Duhamel G, Le Troter A, Feiweier T, Guye M, Ranjeva J, Callot V (2015) Regional and age-related variations of the healthy spinal cord structure assessed by multimodal MRI. In: Proceedings of the 23th annual meeting of ISMRM, Toronto, Canada, p 681

76.

Stroman P, Tomanek B, Krause V, Frankenstein U, Malisza K (2002) Mapping of neuronal function in the healthy and injured human spinal cord with spinal fMRI. Neuroimage 17(4):1854–1860PubMedCrossRef

77.

Stroman PW (2009) Spinal fMRI investigation of human spinal cord function over a range of innocuous thermal sensory stimuli and study-related emotional influences. Magn Reson Imaging 27(10):1333–1346PubMedCrossRef

78.

McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, McFarland HF, Paty DW, Polman CH, Reingold SC, Sandberg-Wollheim M, Sibley W, Thompson A, van den Noort S, Weinshenker BY, Wolinsky JS (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50(1):121–127PubMedCrossRef

79.

Kidd D, Thorpe J, Thompson A, Kendall B, Moseley I, MacManus D, McDonald W, Miller D (1993) Spinal cord MRI using multi-array coils and fast spin echo II. Findings in multiple sclerosis. Neurology 43(12):2632PubMedCrossRef

80.

Bakshi R, Dandamudi VS, Neema M, De C, Bermel RA (2005) Measurement of brain and spinal cord atrophy by magnetic resonance imaging as a tool to monitor multiple sclerosis. J Neuroimaging 15(4 Suppl):30S–45SPubMedCrossRef

81.

Bastianello S, Paolillo A, Giugni E, Giuliani S, Evangelisti G, Luccichenti G, Angeloni U, Colonnese C, Salvetti M, Gasperini C, Pozzilli C, Fieschi C (2000) MRI of spinal cord in MS. J Neurovirol 6(Suppl 2):S130–S133PubMed

82.

Stevenson VL, Leary SM, Losseff NA, Parker GJ, Barker GJ, Husmani Y, Miller DH, Thompson AJ (1998) Spinal cord atrophy and disability in MS: a longitudinal study. Neurology 51(1):234–238PubMedCrossRef

83.

Liu C, Edwards S, Gong Q, Roberts N, Blumhardt LD (1999) Three-dimensional MRI estimates of brain and spinal cord atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 66(3):323–330PubMedPubMedCentralCrossRef

84.

Ingle GT, Stevenson VL, Miller DH, Thompson AJ (2003) Primary progressive multiple sclerosis: a 5-year clinical and MR study. Brain 126(Pt 11):2528–2536PubMedCrossRef

85.

Kalkers NF, Barkhof F, Bergers E, van Schijndel R, Polman CH (2002) The effect of the neuroprotective agent riluzole on MRI parameters in primary progressive multiple sclerosis: a pilot study. Mult Scler 8(6):532–533PubMedCrossRef

86.

Lin X, Blumhardt LD, Constantinescu CS (2003) The relationship of brain and cervical cord volume to disability in clinical subtypes of multiple sclerosis: a three-dimensional MRI study. Acta Neurol Scand 108(6):401–406PubMedCrossRef

87.

Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis an expanded disability status scale (EDSS). Neurology 33(11):1444–1452PubMedCrossRef

88.

Schlaeger R, Papinutto N, Panara V, Bevan C, Lobach IV, Bucci M, Caverzasi E, Gelfand JM, Green AJ, Jordan KM, Stern WA, von Budingen HC, Waubant E, Zhu AH, Goodin DS, Cree BA, Hauser SL, Henry RG (2014) Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann Neurol 76(4):568–580PubMedCrossRef

89.

Yiannakas M, Mustafa A, De Leener B, Cohen-Adad J, Kearney H, Miller D, Wheeler-Kingshott C (2015) Fully automated segmentation of the cervical spinal cord using PropSeg: application to multiple sclerosis. In: Proceedings of the 23th annual meeting of ISMRM, Toronto, Canada, p 4354

90.

Freund P, Wheeler-Kingshott C, Jackson J, Miller D, Thompson A, Ciccarelli O (2010) Recovery after spinal cord relapse in multiple sclerosis is predicted by radial diffusivity. Mult Scler 16(10):1193–1202PubMedPubMedCentralCrossRef

91.

Zackowski KM, Smith SA, Reich DS, Gordon-Lipkin E, Chodkowski BA, Sambandan DR, Shteyman M, Bastian AJ, van Zijl PC, Calabresi PA (2009) Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord. Brain 132(Pt 5):1200–1209PubMedPubMedCentralCrossRef

92.

Wingerchuk DM, Hogancamp WF, O’Brien PC, Weinshenker BG (1999) The clinical course of neuromyelitis optica (Devic’s syndrome). Neurology 53(5):1107–1114PubMedCrossRef

93.

Society NMS (2015) Other Conditions to Rule Out. http://www.nationalmssociety.org/Symptoms-Diagnosis/Other-Conditions-to-Rule-Out. Accessed 25 June 2015

94.

Wang Y, Wu A, Chen X, Zhang L, Lin Y, Sun S, Cai W, Zhang B, Kang Z, Qiu W, Hu X, Lu Z (2014) Comparison of clinical characteristics between neuromyelitis optica spectrum disorders with and without spinal cord atrophy. BMC Neurol 14:246PubMedPubMedCentralCrossRef

95.

Liu Y, Wang J, Daams M, Weiler F, Hahn HK, Duan Y, Huang J, Ren Z, Ye J, Dong H, Vrenken H, Wattjes MP, Shi FD, Li K, Barkhof F (2015) Differential patterns of spinal cord and brain atrophy in NMO and MS. Neurology 84(14):1465–1472PubMedCrossRef

96.

Nakamura M, Miyazawa I, Fujihara K, Nakashima I, Misu T, Watanabe S, Takahashi T, Itoyama Y (2008) Preferential spinal central gray matter involvement in neuromyelitis optica. An MRI study. J Neurol 255(2):163–170PubMedCrossRef

97.

Brooks BR (1996) Natural history of ALS: symptoms, strength, pulmonary function, and disability. Neurology 47(4 Suppl 2):S71–S81 (discussion S81-72) PubMedCrossRef

98.

Brooks BR (1994) El Escorial World Federation of Neurology criteria for the diagnosis of amyotrophic lateral sclerosis. Subcommittee on Motor Neuron Diseases/Amyotrophic Lateral Sclerosis of the World Federation of Neurology Research Group on Neuromuscular Diseases and the El Escorial “Clinical limits of amyotrophic lateral sclerosis” workshop contributors. J Neurol Sci 124(Suppl):96–107PubMedCrossRef

99.

Shefner J, Watson M, Simionescu L, Caress J, Burns T, Maragakis N, Benatar M, David W, Sharma K, Rutkove S (2011) Multipoint incremental motor unit number estimation as an outcome measure in ALS. Neurology 77(3):235–241PubMedPubMedCentralCrossRef

100.

Brownell B, Oppenheimer DR, Hughes JT (1970) The central nervous system in motor neurone disease. J Neurol Neurosurg Psychiatry 33(3):338–357PubMedPubMedCentralCrossRef

101.

Cohen-Adad J, El Mendili MM, Morizot-Koutlidis R, Lehericy S, Meininger V, Blancho S, Rossignol S, Benali H, Pradat PF (2013) Involvement of spinal sensory pathway in ALS and specificity of cord atrophy to lower motor neuron degeneration. Amyotroph Lateral Scler Frontotemporal Degener 14(1):30–38PubMedCrossRef

102.

Tator CH, Fehlings MG (1991) Review of the secondary injury theory of acute spinal cord trauma with emphasis on vascular mechanisms. J Neurosurg 75(1):15–26PubMedCrossRef

103.

Cohen-Adad J, Leblond H, Delivet-Mongrain H, Martinez M, Benali H, Rossignol S (2011) Wallerian degeneration after spinal cord lesions in cats detected with diffusion tensor imaging. Neuroimage 57(3):1068–1076PubMedCrossRef

104.

Cohen-Adad J, El Mendili M, Lehéricy S, Pradat P, Blancho S, Rossignol S, Benali H (2011) Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI. Neuroimage 55(3):1024–1033PubMedCrossRef

105.

El Mendili MM, Chen R, Tiret B, Pelegrini-Issac M, Cohen-Adad J, Lehericy S, Pradat PF, Benali H (2014) Validation of a semiautomated spinal cord segmentation method. J Magn Reson Imaging 41(2):454–459PubMedCrossRef

106.

Rossignol S, Martinez M, Escalona M, Kundu A, Delivet-Mongrain H, Alluin O, Gossard JP (2015) The “beneficial” effects of locomotor training after various types of spinal lesions in cats and rats. Prog Brain Res 218:173–198PubMedCrossRef

107.

Cadotte DW, Fehlings MG (2014) Traumatic spinal cord injury: acute spinal cord injury and prognosis. In: Cohen-Adad J, Wheeler-Kingshott C (eds) Quantitative MRI of the spinal cord. Elsevier, London, pp 39–48CrossRef

108.

Miyanji F, Furlan JC, Aarabi B, Arnold PM, Fehlings MG (2007) Acute cervical traumatic spinal cord injury: MR imaging findings correlated with neurologic outcome—prospective study with 100 consecutive patients 1. Radiology 243(3):820–827PubMedCrossRef

109.

Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14(1 Pt 1):21–36PubMedCrossRef

110.

Cruz-Sanchez FF, Moral A, Tolosa E, de Belleroche J, Rossi ML (1998) Evaluation of neuronal loss, astrocytosis and abnormalities of cytoskeletal components of large motor neurons in the human anterior horn in aging. J Neural Transm 105(6–7):689–701PubMedCrossRef

111.

Valsasina P, Horsfield MA, Rocca MA, Absinta M, Comi G, Filippi M (2012) Spatial normalization and regional assessment of cord atrophy: voxel-based analysis of cervical cord 3D T1-weighted images. AJNR Am J Neuroradiol 33(11):2195–2200PubMedCrossRef

112.

Agosta F, Lagana M, Valsasina P, Sala S, Dall’Occhio L, Sormani MP, Judica E, Filippi M (2007) Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI. Neuroimage 36(3):728–735PubMedCrossRef

113.

MacMillan EL, Madler B, Fichtner N, Dvorak MF, Li DK, Curt A, MacKay AL (2011) Myelin water and T(2) relaxation measurements in the healthy cervical spinal cord at 3.0T: repeatability and changes with age. Neuroimage 54(2):1083–1090PubMedCrossRef

114.

Abdel-Aziz K, Solanky BS, Yiannakas MC, Altmann DR, Wheeler-Kingshott CA, Thompson AJ, Ciccarelli O (2014) Age related changes in metabolite concentrations in the normal spinal cord. PLoS One 9(10):e105774PubMedPubMedCentralCrossRef

115.

Varma G, Duhamel G, de Bazelaire C, Alsop DC (2015) Magnetization transfer from inhomogeneously broadened lines: a potential marker for myelin. Magn Reson Med 73(2):614–622PubMedPubMedCentralCrossRef

116.

BrainWeb (2015) BrainWeb: Simulated Brain Database. http://www.bic.mni.mcgill.ca/brainweb. Accessed 2015-09-03

117.

Lucas BC, Bogovic JA, Carass A, Bazin P-L, Prince JL, Pham DL, Landman BA (2010) The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 8(1):5–17PubMedPubMedCentralCrossRef

118.

Sdika M, Callot V, Hebert M, Duhamel G, Cozzone PJ (2010) Segmentation of the structure of the mouse spinal cord on DTI images. In: Proceedings of the 19th scientific meeting, international society for magnetic resonance in medicine, ISMRM, Stockholm, p 5092

119.

Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20(1):45–57PubMedCrossRef

120.

Mukherjee DP, Cheng I, Ray N, Mushahwar V, Lebel M, Basu A (2010) Automatic segmentation of spinal cord MRI using symmetric boundary tracing. IEEE Trans Inf Technol Biomed 14(5):1275–1278PubMedPubMedCentralCrossRef

121.

Archip N, Erard P-J, Egmont-Petersen M, Haefliger J-M, Germond J-F (2002) A knowledge-based approach to automatic detection of the spinal cord in CT images. IEEE Trans Med Imaging 21(12):1504–1516PubMedCrossRef

122.

Cadotte DW, Cadotte A, Cohen-Adad J, Fleet D, Livne M, Wilson JR, Mikulis D, Nugaeva N, Fehlings MG (2015) Characterizing the location of spinal and vertebral levels in the human cervical spinal cord. AJNR Am J Neuroradiol 36(4):803–810PubMedCrossRef

123.

Altman J, Bayer SA (eds) (2001) An overview of spinal cord organization. In: Development of the human spinal cord: an interpretation based on experimental studies in animals. Oxford University Press, New York, pp 1–87

124.

Wikipedia (2015) Spinal cord. https://en.wikipedia.org/?title=Spinal_cord. Accessed 2015-09-01

125.

Dubuc B (2015) The brain from top to bottom. http://thebrain.mcgill.ca. Accessed 2015-09-22

Title: Segmentation of the human spinal cord
Authors: Benjamin De Leener
Manuel Taso
Julien Cohen-Adad
Virginie Callot
Publication date: 01-04-2016
Publisher: Springer Berlin Heidelberg
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 2/2016
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI: https://doi.org/10.1007/s10334-015-0507-2

At a glance: The STEP trials

Springer Medicine

Segmentation of the human spinal cord

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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