BEaST: Brain extraction based on nonlocal segmentation technique

NeuroImage

Volumn 59, Issue 3, 2012, Pages 2362-2373

  Eskildsen, Simon F ^a,b   Coupé, Pierrick ^a   Fonov, Vladimir ^a   Manjón, José V ^c   Leung, Kelvin K ^d   Guizard, Nicolas ^a   Wassef, Shafik N ^a   Østergaard, Lasse Riis ^b   Collins, D Louis ^a  

^a MCGILL UNIVERSITY   (Canada)

^b AALBORG UNIVERSITY   (Denmark)

^c UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

BET; Brain extraction; MRI; Multi resolution; Patch based segmentation; Skull stripping

Indexed keywords

ACCURACY; ARTICLE; BRAIN; BRAIN DEVELOPMENT; BRAIN EXTRACTION BASED ON NONLOCAL SEGMENTATION TECHNIQUE; DATA BASE; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; HUMAN; IMAGE PROCESSING; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROCESSING TIME; TIME; VALIDATION PROCESS;

ADOLESCENT; ADULT; ALGORITHMS; BRAIN; BRAIN MAPPING; COMPUTERS; DATABASES, FACTUAL; FALSE NEGATIVE REACTIONS; FALSE POSITIVE REACTIONS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; QUALITY CONTROL; REFERENCE STANDARDS; REPRODUCIBILITY OF RESULTS; SOFTWARE; YOUNG ADULT;

EID: 84855429082     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.09.012     Document Type: Article

References (56)

1. Aljabar P., Heckemann R., Hammers A., Hajnal J.V., Rueckert D. Classifier selection strategies for label fusion using large atlas databases. Medical image computing and computer-assisted intervention: MICCAI - International Conference on Medical Image Computing and Computer-Assisted Intervention 2007, 10:523-531.
2. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007, 38:95-113.
3. Ashburner J., Friston K.J. Voxel-based morphometry-the methods. Neuroimage 2000, 11:805-821.
4. Ashburner J., Friston K.J. Computing average shaped tissue probability templates. Neuroimage 2009, 45:333-341.
5. Baillard C., Hellier P., Barillot C. Segmentation of brain 3D MR images using level sets and dense registration. Medical Image Analysis 2001, 5:185-194.
6. Boesen K., Rehm K., Schaper K., Stoltzner S., Woods R., Luders E., Rottenberg D. Quantitative comparison of four brain extraction algorithms. Neuroimage 2004, 22:1255-1261.
7. Buades A., Coll B., Morel J.M. A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 2005, 4:490-530.
8. Carass A., Cuzzocreo J., Wheeler M.B., Bazin P.-L., Resnick S.M., Prince J.L. Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 2011, 56:1982-1992.
9. Collins D.L., Pruessner J.C. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. Neuroimage 2010, 52:1355-1366.
10. Collins D.L., Neelin P., Peters T.M., Evans A.C. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994, 18:192-205.
11. Coupe P., Yger P., Prima S., Hellier P., Kervrann C., Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. Med. Imaging IEEE Trans. 2008, 27:425-441.
12. Coupé P., Manjón J., Fonov V., Pruessner J., Robles M., Collins L. Nonlocal patch-based label fusion for hippocampus segmentation. Medical image computing and computer-assisted intervention: MICCAI - International Conference on Medical Image Computing and Computer-Assisted Intervention 2010, 13:129-136.
13. Coupé P., Manjón J., Fonov V., Pruessner J., Robles M., Collins L. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 2011, 54:940-954.
14. Dale A.M., Fischl B., Sereno M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 1999, 9:179-194.
15. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
16. Eskildsen S.F., Ostergaard L.R. Active surface approach for extraction of the human cerebral cortex from MRI. Medical image computing and computer-assisted intervention: MICCAI - International Conference on Medical Image Computing and Computer-Assisted Intervention 2006, 9:823-830.
17. Evans A.C. The NIH MRI study of normal brain development. Neuroimage 2006, 30:184-202.
18. Fennema-Notestine C., Ozyurt I.B., Clark C.P., Morris S., Bischoff-Grethe A., Bondi M.W., Jernigan T.L., Fischl B., Segonne F., Shattuck D.W., Leahy R.M., Rex D.E., Toga A.W., Zou K.H., Brown G.G. Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Hum. Brain Mapp. 2006, 27:99-113.
19. Fonov V., Evans A.C., Botteron K., Almli C.R., McKinstry R.C., Collins D.L. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011, 54:313-327.
20. Goldszal A.F., Davatzikos C., Pham D.L., Yan M.X., Bryan R.N., Resnick S.M. An image-processing system for qualitative and quantitative volumetric analysis of brain images. J. Comput. Assist. Tomogr. 1998, 22:827-837.
21. Hahn H.K., Peitgen H.-O. The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform. Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention 2000, 134-143. Springer-Verlag.
22. Hartley S.W., Scher A.I., Korf E.S., White L.R., Launer L.J. Analysis and validation of automated skull stripping tools: a validation study based on 296 MR images from the Honolulu Asia aging study. Neuroimage 2006, 30:1179-1186.
23. 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.
24. Iglesias J., Liu C., Thompson P., Tu Z. Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods. Medical Imaging, IEEE Transactions on 2011, 30(9):1617-1634.
25. Kapur T., Grimson W.E., Wells W.M., Kikinis R. Segmentation of brain tissue from magnetic resonance images. Medical image analysis 1996, 1:109-127.
26. Keihaninejad S., Heckemann R.A., Fagiolo G., Symms M.R., Hajnal J.V., Hammers A. A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T). Neuroimage 2010, 50:1427-1437.
27. Lee J.M., Yoon U., Nam S.H., Kim J.H., Kim I.Y., Kim S.I. Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error. Comput. Biol. Med. 2003, 33:495-507.
28. Lemieux L., Hagemann G., Krakow K., Woermann F.G. Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine 1999, 42:127-135.
29. Leung K.K., Barnes J., Ridgway G.R., Bartlett J.W., Clarkson M.J., Macdonald K., Schuff N., Fox N.C., Ourselin S. Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease. Neuroimage 2010, 51:1345-1359.
30. Leung K.K., Barnes J., Modat M., Ridgway G.R., Bartlett J.W., Fox N.C., Ourselin S. Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 2011, 55:1091-1108.
31. Manjon J.V., Carbonell-Caballero J., Lull J.J., Garcia-Marti G., Marti-Bonmati L., Robles M. MRI denoising using non-local means. Medical image analysis 2008, 12:514-523.
32. Mazziotta J.C., Toga A.W., Evans A., Fox P., Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). Neuroimage 1995, 2:89-101.
33. Mikheev A., Nevsky G., Govindan S., Grossman R., Rusinek H. Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm. J. Magn. Reson. Imaging: JMRI 2008, 27:1235-1241.
34. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin. N. Am. 2005, 15:869-877. xi-xii.
35. Nyul L.G., Udupa J.K. Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning 2000, 496-504. SPIE, San Diego, CA, USA. S.K. Mun (Ed.).
36. Palhano Xavier de Fontes F., Andrade Barroso G., Coupé P., Hellier P. Real time ultrasound image denoising. J. Real-Time Image Process. 2011, 6:15-22.
37. Park J.G., Lee C. Skull stripping based on region growing for magnetic resonance brain images. Neuroimage 2009, 47:1394-1407.
38. Rehm K., Schaper K., Anderson J., Woods R., Stoltzner S., Rottenberg D. Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes. Neuroimage 2004, 22:1262-1270.
39. Rex D.E., Shattuck D.W., Woods R.P., Narr K.L., Luders E., Rehm K., Stoltzner S.E., Rottenberg D.A., Toga A.W. A meta-algorithm for brain extraction in MRI. Neuroimage 2004, 23:625-637.
40. Rohlfing T., Brandt R., Menzel R., Maurer C.R. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 2004, 21:1428-1442.
41. Sabuncu M.R., Balci S.K., Shenton M.E., Golland P. Image-driven population analysis through mixture modeling. IEEE Trans. Med. Imaging 2009, 28:1473-1487.
42. Sadananthan S.A., Zheng W., Chee M.W., Zagorodnov V. Skull stripping using graph cuts. NeuroImage 2010, 49:225-239.
43. Sandor S., Leahy R. Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Trans. Med. Imaging 1997, 16:41-54.
44. Segonne F., Dale A.M., Busa E., Glessner M., Salat D., Hahn H.K., Fischl B. A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004, 22:1060-1075.
45. Shan Z.Y., Yue G.H., Liu J.Z. Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. Neuroimage 2002, 17:1587-1598.
46. Shattuck D.W., Sandor-Leahy S.R., Schaper K.A., Rottenberg D.A., Leahy R.M. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2001, 13:856-876.
47. Shattuck D.W., Prasad G., Mirza M., Narr K.L., Toga A.W. Online resource for validation of brain segmentation methods. Neuroimage 2009, 45:431-439.
48. Sled J.G., Zijdenbos A.P., Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17:87-97.
49. Smith S.M. Fast robust automated brain extraction. Hum. Brain Mapp. 2002, 17:143-155.
50. 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., Niazy R.K., Saunders J., Vickers J., Zhang Y., De Stefano N., Brady J.M., Matthews P.M. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004, 23(Suppl 1):S208-S219.
51. van der Kouwe A.J., Benner T., Salat D.H., Fischl B. Brain morphometry with multiecho MPRAGE. Neuroimage 2008, 40:559-569.
52. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society 2004, 13:600-612.
53. Wang H., Das S., Suh J.W., Altinay M., Pluta J., Craige C., Avants B., Yushkevich P. A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation. Neuroimage 2011, 55:968-985.
54. Ward B.D. 3dIntracranial: Automatic segmentation of intracranial region 1999.
55. Warfield S.K., Zou K.H., Wells W.M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Med. Imaging IEEE Trans. 2004, 23:903-921.
56. Zhuang A.H., Valentino D.J., Toga A.W. Skull-stripping magnetic resonance brain images using a model-based level set. Neuroimage 2006, 32:79-92.