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A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

Juan Eugenio Iglesias, Ricardo Insausti, Garikoitz Lerma-Usabiaga and 7 more

NeuroImage · 2018 · CC BY

doi:10.1016/j.neuroimage.2018.08.012

Abstract

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.

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References 90

  1. 1.

    Abivardi A., Bach D.R. Deconstructing white matter connectivity of human amygdala nuclei with thalamus and cortex subdivisions in vivo. Hum. Brain Mapp. 2017;38:3927-3940.

    Not in our library10.1002/hbm.23639

  2. 2.

    Adler D.H., Wisse L.E., Ittyerah R., Pluta J.B., Ding S.L., Xie L., Wang J., Kadivar S., Robinson J.L., Schuck T., Trojanowski J.Q., Grossman M., Detre J.A., Elliott M.A., Toledo J.B., Liu W., Pickup S., Miller M.I., Das S.R., Wolk D.A., Yushkevich P.A. Characterizing the human hippocampus in aging and alzheimers disease using a computational atlas derived from ex vivo MRI and histology. Proc. Natl. Acad. Sci. Unit. States Am. 2018;115:4252-4257.

    Not in our library10.1073/pnas.1801093115

  3. 3.
  4. 4.
  5. 5.

    Amini L., Soltanian-Zadeh H., Lucas C., Gity M. Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours. IEEE Trans. Biomed. Eng. 2004;51:800-811.

    Not in our library10.1109/tbme.2004.826654

  6. 6.

    Amunts K., Lepage C., Borgeat L., Mohlberg H., Dickscheid T., Rousseau M.É., Bludau S., Bazin P.L., Lewis L.B., Oros-Peusquens A.M. Bigbrain: an ultrahigh-resolution 3D human brain model. Science. 2013;340:1472-1475.

    Not in our library10.1126/science.1235381

  7. 7.

    Andreasen N.C., Arndt S., Swayze V., Cizadlo T., Flaum M., O'Leary D., Ehrhardt J.C., Yuh W. Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. Science. 1994;266:294-298.

    Not in our library10.1126/science.7939669

  8. 8.

    Aron A., Schlaghecken F., Fletcher P., Bullmore E.T., Eimer M., Barker R., Sahakian B., Robbins T. Inhibition of subliminally primed responses is mediated by the caudate and thalamus: evidence from functional MRI and huntingtons disease. Brain. 2003;126:713-723.

    Not in our library10.1093/brain/awg067

  9. 9.

    Ashburner J., Friston K.J. Unified segmentation. Neuroimage. 2005;26:839-851.

    Not in our library10.1016/j.neuroimage.2005.02.018

  10. 10.

    Ashburner J., Andersson J.L., Friston K.J. Image registration using a symmetric priorin three dimensions. Hum. Brain Mapp. 2000;9:212-225.

    Not in our library10.1002/(sici)1097-0193(200004)9:4<212::aid-hbm3>3.0.co;2-#

  11. 11.

    Avants B.B., Epstein C.L., Grossman M., Gee J.C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 2008;12:26-41.

    Not in our library10.1016/j.media.2007.06.004

  12. 12.

    Battistella G., Najdenovska E., Maeder P., Ghazaleh N., Daducci A., Thiran J.P., Jacquemont S., Tuleasca C., Levivier M., Cuadra M.B. Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties. Brain Struct. Funct. 2017;222:2203-2216.

    Not in our library10.1007/s00429-016-1336-4

  13. 13.

    Bay H., Tuytelaars T., Van Gool L. SURF: speeded up robust features. European Conference on Computer Vision. 2006;404-417.

    Not in our library

  14. 14.

    Behrens T., Johansen-Berg H., Woolrich M., Smith S., Wheeler-Kingshott C., Boulby P., Barker G., Sillery E., Sheehan K., Ciccarelli O. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 2003;6:750-757.

    Not in our library10.1038/nn1075

  15. 15.

    Binder J.R., Frost J.A., Hammeke T.A., Cox R.W., Rao S.M., Prieto T. Human brain language areas identified by functional magnetic resonance imaging. J. Neurosci. 1997;17:353-362.

    Not in our library10.1523/jneurosci.17-01-00353.1997

  16. 16.

    Bonilha L., Rorden C., Castellano G., Cendes F., Li L.M. Voxel-based morphometry of the thalamus in patients with refractory medial temporal lobe epilepsy. Neuroimage. 2005;25:1016-1021.

    Not in our library10.1016/j.neuroimage.2004.11.050

  17. 17.

    Boykov Y., Veksler O., Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 2001;23:1222-1239.

    Not in our library

  18. 18.

    Braak H., Braak E. Alzheimer's disease affects limbic nuclei of the thalamus. Acta Neuropathol. 1991;81:261-268.

    Not in our library10.1007/bf00305867

  19. 19.

    Breiman L. Random forests. Mach. Learn. 2001;45:5-32.

    Not in our library

  20. 20.

    Buchsbaum M.S., Someya T., Teng C.Y., Abel L., Chin S., Najafi A., Haier R.J., Wu J., Bunney W.E. PET and MRI of the thalamus in never-medicated patients with schizophrenia. Am. J. Psychiatr. 1996;153:191-199.

    Not in our library10.1176/ajp.153.2.191

  21. 21.

    Cash D.M., Ridgway G.R., Liang Y., Ryan N.S., Kinnunen K.M., Yeatman T., Malone I.B., Benzinger T.L., Jack C.R., Thompson P.M. The pattern of atrophy in familial alzheimer disease volumetric MRI results from the DIAN study. Neurology. 2013;81:1425-1433.

    Not in our library10.1212/wnl.0b013e3182a841c6

  22. 22.

    Caviness V.S., Filipek P.A., Kennedy D.N. Magnetic resonance technology in human brain science: blueprint for a program based upon morphometry. Brain Dev. 1989;11:1-13.

    Not in our library10.1016/s0387-7604(89)80002-6

  23. 23.

    Cho H., Kim J.H., Kim C., Ye B.S., Kim H.J., Yoon C.W., Noh Y., Kim G.H., Kim Y.J., Kim J.H. Shape changes of the basal ganglia and thalamus in alzheimer's disease: a three-year longitudinal study. J. Alzheim. Dis. 2014;40:285-295.

    Not in our library10.3233/jad-132072

  24. 24.

    Criminisi A., Shotton J. Decision Forests for Computer Vision and Medical Image Analysis. 2013.

    Not in our library

  25. 25.

    Czisch M., Wehrle R., Kaufmann C., Wetter T.C., Holsboer F., Pollmächer T., Auer D.P. Functional MRI during sleep: BOLD signal decreases and their electrophysiological correlates. Eur. J. Neurosci. 2004;20:566-574.

    Not in our library10.1111/j.1460-9568.2004.03518.x

  26. 26.

    De Jong L., Van der Hiele K., Veer I., Houwing J., Westendorp R., Bollen E., De Bruin P., Middelkoop H., Van Buchem M., Van Der Grond J. Strongly reduced volumes of putamen and thalamus in alzheimer's disease: an MRI study. Brain. 2008;131:3277-3285.

    Not in our library10.1093/brain/awn278

  27. 27.

    DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;837-845.

    Not in our library

  28. 28.

    Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B. 1977;1-38.

    Not in our library

  29. 29.

    Díaz B., Hintz F., Kiebel S.J., von Kriegstein K. Dysfunction of the auditory thalamus in developmental dyslexia. Proc. Natl. Acad. Sci. Unit. States Am. 2012;109:13841-13846.

    Not in our library10.1073/pnas.1119828109

  30. 30.

    Duan Y., Li X., Xi Y. Thalamus segmentation from diffusion tensor magnetic resonance imaging. Journal of Biomedical Imaging. 2007;391-401.

    Not in our library10.1155/2007/90216

  31. 31.

    Fernández-Espejo D., Junque C., Bernabeu M., Roig-Rovira T., Vendrell P., Mercader J.M. Reductions of thalamic volume and regional shape changes in the vegetative and the minimally conscious states. J. Neurotrauma. 2010;27:1187-1193.

    Not in our library10.1089/neu.2010.1297

  32. 32.

    Fischl B. Freesurfer. Neuroimage. 2012;62:774-781.

    Not in our library10.1016/j.neuroimage.2012.01.021

  33. 33.

    Fischl B., Salat D.H., Busa E., Albert M., Dieterich M., Haselgrove C., Van Der Kouwe A., Killiany R., Kennedy D., Klaveness S. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341-355.

    Not in our library10.1016/s0896-6273(02)00569-x

  34. 34.

    Fischler M.A., Bolles R.C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Readings in Computer Vision. 1987;726-740.

    Not in our library

  35. 35.

    Fisher R.A. The use of multiple measurements in taxonomic problems. Ann. Hum. Genet. 1936;7:179-188.

    Not in our library

  36. 36.

    Fukunaga K., Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theor. 1975;21:32-40.

    Not in our library

  37. 37.

    Geremia E., Clatz O., Menze B.H., Konukoglu E., Criminisi A., Ayache N. Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images. Neuroimage. 2011;57:378-390.

    Not in our library10.1016/j.neuroimage.2011.03.080

  38. 38.

    Giraldo-Chica M., Hegarty J.P., Schneider K.A. Morphological differences in the lateral geniculate nucleus associated with dyslexia. Neuroimage: Clinica. 2015;7:830-836.

    Not in our library10.1016/j.nicl.2015.03.011

  39. 39.

    Guye M., Parker G.J., Symms M., Boulby P., Wheeler-Kingshott C.A., Salek-Haddadi A., Barker G.J., Duncan J.S. Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo. Neuroimage. 2003;19:1349-1360.

    Not in our library10.1016/s1053-8119(03)00165-4

  40. 40.

    Hale J.R., Mayhew S.D., Mullinger K.J., Wilson R.S., Arvanitis T.N., Francis S.T., Bagshaw A.P. Comparison of functional thalamic segmentation from seed-based analysis and ICA. Neuroimage. 2015;114:448-465.

    Not in our library10.1016/j.neuroimage.2015.04.027

  41. 41.

    Heckemann R.A., Hajnal J.V., Aljabar P., Rueckert D., Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage. 2006;33:115-126.

    Not in our library10.1016/j.neuroimage.2006.05.061

  42. 42.

    Holmes C.J., Hoge R., Collins L., Woods R., Toga A.W., Evans A.C. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 1998;22:324-333.

    Not in our library10.1097/00004728-199803000-00032

  43. 43.

    Iglesias J.E., Augustinack J.C., Nguyen K., Player C.M., Player A., Wright M., Roy N., Frosch M.P., McKee A.C., Wald L.L. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage. 2015;115:117-137.

    Not in our library10.1016/j.neuroimage.2015.04.042

  44. 44.

    Iglesias J.E., Van Leemput K., Bhatt P., Casillas C., Dutt S., Schuff N., Truran-Sacrey D., Boxer A., Fischl B., Initiative A.D.N. Bayesian segmentation of brainstem structures in MRI. Neuroimage. 2015;113:184-195.

    Not in our library10.1016/j.neuroimage.2015.02.065

  45. 45.

    Iglesias J.E., Paz-Alonso P., Lerma-Usabiaga G., Insausti R., Miller K., Caballero-Gaudes C. Simultaneous bayesian correction of slab boundary artifacts and bias field for high resolution ex vivo MRI. 2016 IEEE 13th International Symposium on Biomedical Imaging. 2016;1384-1387.

    Not in our library

  46. 46.

    Jakab A., Blanc R., Berenyi E., Székely G. Generation of individualized thalamus target maps by using statistical shape models and thalamocortical tractography. Am. J. Neuroradiol. 2012;33:2110-2116.

    Not in our library10.3174/ajnr.a3140

  47. 47.

    Jednorog K., Marchewka A., Altarelli I., Monzalvo Lopez A.K., van Ermingen-Marbach M., Grande M., Grabowska A., Heim S., Ramus F. How reliable are gray matter disruptions in specific reading disability across multiple countries and languages? insights from a large-scale voxel-based morphometry study. Hum. Brain Mapp. 2015;36:1741-1754.

    Not in our library10.1002/hbm.22734

  48. 48.

    Ji B., Li Z., Li K., Li L., Langley J., Shen H., Nie S., Zhang R., Hu X. Dynamic thalamus parcellation from resting-state fMRI data. Hum. Brain Mapp. 2016;37:954-967.

    Not in our library10.1002/hbm.23079

  49. 49.

    Johansen-Berg H., Behrens T.E., Sillery E., Ciccarelli O., Thompson A.J., Smith S.M., Matthews P.M. Functional–anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cerebr. Cortex. 2005;15:31-39.

    Not in our library10.1093/cercor/bhh105

  50. 50.

    Jonasson L., Hagmann P., Pollo C., Bresson X., Wilson C.R., Meuli R., Thiran J.P. A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Process. 2007;87:309-321.

    Not in our library

  51. 51.

    Jones E.G. The Thalamus. 2012.

    Not in our library

  52. 52.

    Kasenburg N., Darkner S., Hahn U., Liptrot M., Feragen A. Structural parcellation of the thalamus using shortest-path tractography. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE. 2016;559-563.

    Not in our library

  53. 53.

    Kassubek J., Juengling F.D., Ecker D., Landwehrmeyer G.B. Thalamic atrophy in huntington's disease co-varies with cognitive performance: a morphometric MRI analysis. Cerebr. Cortex. 2005;15:846-853.

    Not in our library10.1093/cercor/bhh185

  54. 54.

    Kirby E., Bandelow S., Hogervorst E. Visual impairment in alzheimer's disease: a critical review. J. Alzheim. Dis. 2010;21:15-34.

    Not in our library10.3233/jad-2010-080785

  55. 55.

    Klein S., Staring M., Murphy K., Viergever M.A., Pluim J.P. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imag. 2010;29:196-205.

    Not in our library10.1109/tmi.2009.2035616

  56. 56.

    Krauth A., Blanc R., Poveda A., Jeanmonod D., Morel A., Székely G. A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage. 2010;49:2053-2062.

    Not in our library10.1016/j.neuroimage.2009.10.042

  57. 57.

    Lambert C., Simon H., Colman J., Barrick T.R. Defining thalamic nuclei and topographic connectivity gradients in vivo. Neuroimage. 2017;158:466-479.

    Not in our library10.1016/j.neuroimage.2016.08.028

  58. 58.

    Ma D., Gulani V., Seiberlich N., Liu K., Sunshine J.L., Duerk J.L., Griswold M.A. Magnetic resonance fingerprinting. Nature. 2013;495:187.

    Not in our library10.1038/nature11971

  59. 59.

    Mai J.K., Forutan F. Thalamus. The Human Nervous System. 2012;618-677.

    Not in our library

  60. 60.

    Malandain G., Bardinet E., Nelissen K., Vanduffel W. Fusion of autoradiographs with an MR volume using 2-D and 3-D linear transformations. Neuroimage. 2004;23:111-127.

    Not in our library10.1016/j.neuroimage.2004.04.038

  61. 61.

    Mang S.C., Busza A., Reiterer S., Grodd W. Thalamus segmentation based on the local diffusion direction: a group study. Magn. Reson. Med. 2012;67:118-126.

    Not in our library10.1002/mrm.22996

  62. 62.

    Marques J.P., Kober T., Krueger G., van der Zwaag W., Van de Moortele P.F., Gruetter R. Mp2rage, a self bias-field corrected sequence for improved segmentation and t1-mapping at high field. Neuroimage. 2010;49:1271-1281.

    Not in our library10.1016/j.neuroimage.2009.10.002

  63. 63.

    McNab J.A., Jbabdi S., Deoni S.C., Douaud G., Behrens T.E., Miller K.L. High resolution diffusion-weighted imaging in fixed human brain using diffusion-weighted steady state free precession. Neuroimage. 2009;46:775-785.

    Not in our library10.1016/j.neuroimage.2009.01.008

  64. 64.

    Middlebrooks E.H., Holanda V.M., Tuna I.S., Deshpande H.D., Bredel M., Almeida L., Walker H.C., Guthrie B.L., Foote K.D., Okun M.S. A method for pre-operative single-subject thalamic segmentation based on probabilistic tractography for essential tremor deep brain stimulation. Neuroradiology. 2018;1-7.

    Not in our library10.1007/s00234-017-1972-2

  65. 65.

    Morel A. Stereotactic Atlas of the Human Thalamus and Basal Ganglia. 2007.

    Not in our library

  66. 66.

    Morey R.A., Selgrade E.S., Wagner H.R., Huettel S.A., Wang L., McCarthy G. Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Hum. Brain Mapp. 2010;31:1751-1762.

    Not in our library10.1002/hbm.20973

  67. 67.

    Natsume J., Bernasconi N., Andermann F., Bernasconi A. MRI volumetry of the thalamus in temporal, extratemporal, and idiopathic generalized epilepsy. Neurology. 2003;60:1296-1300.

    Not in our library10.1212/01.wnl.0000058764.34968.c2

  68. 68.

    Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975;11:23-27.

    Not in our library

  69. 69.

    Patenaude B., Smith S.M., Kennedy D.N., Jenkinson M. A bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56:907-922.

    Not in our library10.1016/j.neuroimage.2011.02.046

  70. 70.

    Pichat J., Iglesias J.E., Yousry T., Ourselin S., Modat M. A survey of methods for 3D histology reconstruction. Med. Image Anal. 2018;46:73-105.

    Not in our library10.1016/j.media.2018.02.004

  71. 71.

    Pini L., Pievani M., Bocchetta M., Altomare D., Bosco P., Cavedo E., Galluzzi S., Marizzoni M., Frisoni G.B. Brain atrophy in alzheimers disease and aging. Ageing Res. Rev. 2016;30:25-48.

    Not in our library10.1016/j.arr.2016.01.002

  72. 72.

    Pohl K.M., Fisher J., Grimson W.E.L., Kikinis R., Wells W.M. A bayesian model for joint segmentation and registration. Neuroimage. 2006;31:228-239.

    Not in our library10.1016/j.neuroimage.2005.11.044

  73. 73.

    Puonti O., Iglesias J.E., Van Leemput K. Fast and sequence-adaptive whole-brain segmentation using parametric bayesian modeling. Neuroimage. 2016;143:235-249.

    Not in our library10.1016/j.neuroimage.2016.09.011

  74. 74.

    Sadikot A.F., Chakravarty M.M., Bertrand G., Rymar V.V., Al-Subaie F., Collins D.L. Creation of computerized 3D MRI-integrated atlases of the human basal ganglia and thalamus. Front. Syst. Neurosci. 2011;5:71.

    Not in our library10.3389/fnsys.2011.00071

  75. 75.

    Saygin Z., Kliemann D., Iglesias J., van der Kouwe A.J., Boyd E., Reuter M., Stevens A., Van Leemput K., McKee A., Frosch M.P. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage. 2017;155:370-382.

    Not in our library10.1016/j.neuroimage.2017.04.046

  76. 76.

    Sherman S.M. The thalamus is more than just a relay. Curr. Opin. Neurobiol. 2007;17:417-422.

    Not in our library10.1016/j.conb.2007.07.003

  77. 77.

    Sherman S.M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 2016;19:533.

    Not in our library10.1038/nn.4269

  78. 78.

    Sherman S.M., Guillery R.W. Exploring the Thalamus. 2001.

    Not in our library

  79. 79.

    Smith S.M., Jenkinson M., Woolrich M.W., Beckmann C.F., Behrens T.E., Johansen-Berg H., Bannister P.R., De Luca M., Drobnjak I., Flitney D.E. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208-S219.

    Not in our library10.1016/j.neuroimage.2004.07.051

  80. 80.

    Stepán-Buksakowska I., Szabó N., Hořínek D., Tóth E., Hort J., Warner J., Charvát F., Vécsei L., Roček M., Kincses Z.T. Cortical and subcortical atrophy in alzheimer disease: parallel atrophy of thalamus and hippocampus. Alzheimer Dis. Assoc. Disord. 2014;28:65-72.

    Not in our library10.1097/wad.0b013e318299d3d6

  81. 81.

    Stough J.V., Glaister J., Ye C., Ying S.H., Prince J.L., Carass A. Automatic method for thalamus parcellation using multi-modal feature classification. International Conference on Medical Image Computing and Computer-assisted Intervention. 2014;169-176.

    Not in our library10.1007/978-3-319-10443-0_22

  82. 82.

    van der Kouwe A.J., Benner T., Salat D.H., Fischl B. Brain morphometry with multiecho MPRAGE. Neuroimage. 2008;40:559-569.

    Not in our library10.1016/j.neuroimage.2007.12.025

  83. 83.

    Van Leemput K. Encoding probabilistic brain atlases using bayesian inference. IEEE Trans. Med. Imag. 2009;28:822-837.

    Not in our library10.1109/tmi.2008.2010434

  84. 84.

    Van Leemput K., Maes F., Vandermeulen D., Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imag. 1999;18:897-908.

    Not in our library10.1109/42.811270

  85. 85.

    Wells W.M., Grimson W.E.L., Kikinis R., Jolesz F.A. Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 1996;15:429-442.

    Not in our library10.1109/42.511747

  86. 86.

    Wiegell M.R., Tuch D.S., Larsson H.B., Wedeen V.J. Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage. 2003;19:391-401.

    Not in our library10.1016/s1053-8119(03)00044-2

  87. 87.

    Xuereb J., Perry R., Candy J., Perry E., Bonham J. Nerve cell loss in the thalamus in alzheimer's disease and Parkinson's disease. Brain. 1991;114:1363-1379.

    Not in our library

  88. 88.

    Zarei M., Patenaude B., Damoiseaux J., Morgese C., Smith S., Matthews P.M., Barkhof F., Rombouts S., Sanz-Arigita E., Jenkinson M. Combining shape and connectivity analysis: an MRI study of thalamic degeneration in alzheimer's disease. Neuroimage. 2010;49:1-8.

    Not in our library10.1016/j.neuroimage.2009.09.001

  89. 89.

    Zhang Y., Brady M., Smith S. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 2001;20:45-57.

    Not in our library10.1109/42.906424

  90. 90.

    Zikic D., Glocker B., Criminisi A. Atlas encoding by randomized forests for efficient label propagation. International Conference on Medical Image Computing and Computer-assisted Intervention. 2013;66-73.

    Not in our library10.1007/978-3-642-40760-4_9

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