Reliable selection of the number of fascicles in diffusion images by estimation of the generalization error

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7917 LNCS, Issue , 2013, Pages 742-753

  Scherrer, Benoit ^a   Taquet, Maxime ^a   Warfield, Simon K ^a  

^a BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROPRIATE MODELS; BOOTSTRAP TECHNIQUE; DECISION THRESHOLD; DIFFUSION SIGNALS; GENERALIZATION ERROR; GENERATIVE MODEL; MODEL ESTIMATION; PARAMETRIC MODELS;

DATA PROCESSING; DIFFUSION; DIFFUSION TENSOR IMAGING; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; TENSORS;

ESTIMATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; CONNECTOME; DIFFUSION TENSOR IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; BRAIN; CONNECTOME; DATA INTERPRETATION, STATISTICAL; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84901278978     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38868-2_62     Document Type: Conference Paper

References (13)

1. Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48(2), 331-340 (2002)
2. Assaf, Y., Basser, P.J.: Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage 27(1), 48-58 (2005)
3. Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34(1), 144-155 (2007)
4. Efron, B., Tibshirani, R.: Improvements on cross-validation: The .632 + bootstrap method. Journal of the American Statistical Association 92(438), 548-560 (1997)
5. Kreher, B., Schneider, J., Mader, I., Martin, E., Hennig, J., Il'yasov, K.: Multitensor approach for analysis and tracking of complex fiber configurations. Magnetic Resonance in Medicine 54(5), 1216-1225 (2005)
6. Miller, K.L., et al.: Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. Neuroimage 57(1), 167-181 (2011)
7. Molinaro, A., Simon, R., Pfeiffer, R.: Prediction error estimation: a comparison of resampling methods. Bioinformatics 21(15), 3301-3307 (2005)
8. Pasternak, O., Westin, C., Bouix, S., Seidman, L., Goldstein, J., Woo, T., Petryshen, T., Mesholam-Gately, R., McCarley, R., Kikinis, R., et al.: Excessive extracellular volume reveals a neurodegenerative pattern in schizophrenia onset. The Journal of Neuroscience 32(48), 17365-17372 (2012)
9. Scherrer, B., Warfield, S.K.: Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI. PLoS ONE 7(11) (2012)
10. Schultz, T., Westin, C.-F., Kindlmann, G.: Multi-diffusion-tensor fitting via spherical deconvolution: A unifying framework. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 674-681. Springer, Heidelberg (2010)
11. Shao, J.: Linear Model Selection by Cross-Validation. Journal of the American Statistical Association 88(442), 486-494 (1993)
12. Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. MRM 48(4), 577-582 (2002)
13. Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: Noddi: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000-1016 (2012)