Assessing the performance of atlas-based prefrontal brain parcellation in an aging cohort

Journal of Computer Assisted Tomography

Volumn 37, Issue 2, 2013, Pages 257-264

  Aribisala, Benjamin S ^a   Cox, Simon R ^a   Ferguson, Karen J ^a   Macpherson, Sarah E ^a   Maclullich, Alasdair M J ^a   Royle, Natalie A ^a   Valdés Hernández, Maria C ^a   Bastin, Mark E ^a   Deary, Ian J ^a   Wardlaw, Joanna M ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Aging; Brain atrophy; MRI; Multi atlas; Prefrontal brain; Skull thickening

Indexed keywords

CEREBROSPINAL FLUID;

AGING POPULATION; BRAIN ATROPHY; DICE COEFFICIENT; INTRACLASS CORRELATION COEFFICIENTS; MULTI-ATLAS; PERFORMANCE; PREFRONTAL BRAIN; SKULL THICKENING; VISUAL ASSESSMENTS; VOLUMETRICS;

MAGNETIC RESONANCE IMAGING;

AGED; AGING; ARTICLE; ATLAS BASED PREFRONTAL BRAIN PARCELLATION; BRAIN SCINTISCANNING; FRONTAL LOBE; GRAY MATTER; HOSPITAL ANXIETY AND DEPRESSION SCALE; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; INSULA; INTERMETHOD COMPARISON; INTRARATER RELIABILITY; MALE; MINI MENTAL STATE EXAMINATION; MOTOR CORTEX; NEUROIMAGING; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PERFORMANCE MEASUREMENT SYSTEM; PRIMARY MOTOR CORTEX; PRIORITY JOURNAL;

EID: 84875449839     PISSN: 03638715     EISSN: 15323145     Source Type: Journal    
DOI: 10.1097/RCT.0b013e31828004ea     Document Type: Article

References (48)

1. Cabezas M, Oliver A, Llado X, et al. A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Prog Biomed. 2011;104:e158-e177
2. Fischl B, Van Der Kouwe A, Destrieux C, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11-22
3. Heckemann RA, Keihaninejad S, Aljabar P, et al. Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-Atlas based anatomical segmentation. Neuroimage. 2010;51:221-227
4. Aljabar P, Heckemann RA, Hammers A, et al. Multi-Atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage. 2009;46:726-738
5. Ashburner J, Friston K. Unified segmentation. Neuroimage. 2005;26:839
6. Moghaddam MJ, Soltanian-Zadeh H. Automatic segmentation of brain structures using geometric moment invariants and artificial neural networks. In: Prince JL, Pham DL, Myers KJ, ed. Information Processing in Medical Imaging, Proceedings. 2009:326-337
7. Babalola KO, Cootes TF, Twining CJ, et al. 3D brain segmentation using active appearance models and local regressors. In: Metaxas D, Axel L, Fichtinger G, et al, eds. Proceedings, International Conference on Medical Image Computing and Computer Assisted Intervention, New York, Lecture Notes in Computer Science. 2008;11:401-408
8. Kelemen A, Szekely G, Gerig G. Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans Med Imaging. 1999;18:828-839
9. Leventon ME, Faugeras O, Grimson WEL, et al. Level set based segmentation with intensity and curvature priors. Hilton Head ISL, SC: EEE Computer Society, Proceedings, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis; 2000:4-11
10. Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage. 2006;31:1116-1128
11. Ginneken B, Heimann T, Styner M. 3D segmentation in the clinic: A grand challenge. Proceeding, International Conference on Medical Image Computing and Computer Assisted Intervention, Brisbane, Australia, 2007:7-15
12. Igual L, Soliva JC, Hernandez-Vela A, et al. A fully-Automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder. Biomed Eng Online. 2011;10:105
13. Babalola KO, Patenaude B, Aljabar P, et al. An evaluation of four automatic methods of segmenting the subcortical structures in the brain (vol 47, pg 1435, 2009). Neuroimage. 2009;49:1152
14. Babalola KO, Patenaude B, Aljabar P, et al. Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI. Medical Image Computing and Computer-Assisted Intervention: MICCAIVInternational Conference on Medical Image Computing and Computer-Assisted Intervention. 2008;11:409-416
15. Aribisala BS, He J, Blamire AM. Comparative study of standard space and real space analysis of quantitative MR brain data. J Magn Reson Imaging. 2011;33:1503-1509
16. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5:143-156
17. Ardekani BA, Guckemus S, Bachman A, et al. Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J Neurosci Methods. 2005;142:67-76
18. Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033-2044
19. Rohlfing T, Brandt R, Menzel R, et al. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage. 2004;21:1428-1442
20. Appelman APA, Exalto LG, Van Der Graaf Y, et al. White matter lesions and brain atrophy: More than shared risk factors? A systematic review. Cerebrovasc Dis. 2009;28:227-242
21. Finby N, Kraft E. The aging skull: Comparative roentgen study. 25 to 34 year interval. Clin Radiol. 1972;23:410-414
22. May H, Peled N, Dar G, et al. Hyperostosis frontalis interna: What does it tell us about our health? Am J Hum Biol. 2011;23:392-397
23. Debette S, Markus H. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-Analysis. BMJ. 2010:26
24. Appelman APA, Vincken KL, Van Der Graaf Y, et al. White matter lesions and lacunar infarcts are independently and differently associated with brain atrophy: The SMART-MR study. Cerebrovasc Dis. 2010;29:28-35
25. Raz N, Lindenberger L, Rodrigue KM, et al. Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cereb Cortex. 2005;15:676-1689
26. Steele JD, Lawrie SM. Segregation of cognitive and emotional function in the prefrontal cortex: A stereotactic meta-Analysis. Neuroimage. 2004;21:868-875
27. Desikan RS, Segonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968-980
28. Destrieux C, Fischl B, Dale A, et al. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53:1-15
29. Deary IJ, Gow AJ, Taylor MD, et al. The Lothian Birth Cohort 1936: A study to examine influences on cognitive ageing from age 11 to age 70 and beyond. BMC Geriatr. 2007;7:28
30. Deary IJ, Gow AJ, Pattie A, et al. Cohort profile: The Lothian Birth Cohorts of 1921 and 1936. Int J Epidemiol. 2012. ;41:1576-1584
31. Scottish Council for Research in Education. The Trend of Scottish Cognitive Ability: A Comparison of the 1947 and 1932 Surveys of the Cognitive Ability of Eleven-Year-Old Pupils. London: University Publishing Group; 1949
32. Wardlaw JM, Bastin ME, Hernandez MCV, et al. Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: Rationale, design and methodology of the imaging protocol. Int J Stroke. 2011;6:547-559
33. Mayo Clinic. Analyze 8.1. AnalyzeDirect, Inc. Mayo Clinic. 2008. Available at: Www.analyzedirect.com/Analyze/upgrade.asp. Accessed August 15, 2012
34. Ferguson KJ, Wardlaw JM. Brain volume measurement in Analyze 8. Available at: Http://www.sbirc.ed.ac.uk/documents/ brain%20volume%20measurement%20in%20analyze%208.pdf. Accessed July 11, 2012
35. Kates WR, Frederikse M, Mostofsky SH, et al. MRI parcellation of the frontal lobe in boys with attention deficit hyperactivity disorder or Tourette syndrome. Psychiatry Res. 116:63-81
36. Hernandez MDV, Ferguson KJ, Chappell FM, et al. New multispectral MRI data fusion technique for white matter lesion segmentation: Method and comparison with thresholding in FLAIR images. Eur Radiol. 2010;20:1684-1691
37. Hernández MC, Royle NA, Jackson NA, et al. Color fusion of magnetic resonance images improves intracranial volume measurement in studies of ageing. Open J Radiol. 2012;2:1-9
38. Rorden C, Brett M. Stereotaxic display of brain lesions. Behav Neurol. 2000;12:191-200
39. Klein A, Andersson J, Ardekani BA, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage. 2009;46:786-802
40. Leung KK, Barnes J, Modat M, et al. Brain MAPS: An automated, accurate and robust brain extraction technique using a template library. Neuroimage. 2011;55:1091-1108
41. Efron B, Tibshirani RJ. An Introduction to Boostrap. London: Chapman and Hall; 1993
42. Collignon A, Maes F, Delaere D, et al. Automated multi-modality image registration based on information theory. In: Barillot C, Di Paola R, eds. Proceedings of Information Processing in Medical Imaging. Brest, France; 1995:263-274
43. Studholme C, Hill DLG, Hawkes DJ. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 1999;32:71-86
44. Shrout PE, Fleiss JL. Intraclass correlationsVused in assessing rater reliability. Psychol Bull. 1979;86:420-428
45. Bland JM, Altman DG. Statistical methods for assessing agreement between 2 methods of clinical measurement. Lancet. 1986;1:307-310
46. Gee JC, Reivich M, Bajcsy R. Elastically deforming 3D Atlas to match anatomical brain images. J Comput Assist Tomogr. 1993;17:225-236
47. Jaccard P. The distribution of flora in the alpine zone. New Phytol. 1912;11:37-50
48. Zijdenbos AP, Dawant BM, Margolin RA, et al. Morphometric analysis of white-matter lesions in MR-imagesVmethod and validation. IEEE Trans Med Imaging. 1994;13:716-724