A validation framework for probabilistic maps using Heschl's gyrus as a model

NeuroImage

Volumn 50, Issue 2, 2010, Pages 532-544

  Tahmasebi, Amir M ^a   Abolmaesumi, Purang ^b   Wild, Conor ^c   Johnsrude, Ingrid S ^a,c  

^a QUEEN S UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c QUEEN S UNIVERSITY   (Canada)

Author keywords

BSpline based groupwise; DARTEL; Goodness of fit; Groupwise registration; HAMMER; Heschl's gyrus; Implicit reference based groupwise; Overlap measure; Pairwise registration; Positive predictive value; Primary auditory cortex; Probabilistic map; Sensitivity; Specificity; Unified segmentation

Indexed keywords

ACCURACY; ADULT; ANALYTICAL PARAMETERS; ARTICLE; BRAIN CORTEX; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; FEMALE; HESCHL GYRUS; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; IMAGE PROCESSING; INTERMETHOD COMPARISON; MALE; NEUROANATOMY; NEUROIMAGING; NORMAL HUMAN; PREDICTIVE VALUE; PRIORITY JOURNAL; PROBABILISTIC MAP; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS; ADOLESCENT; AUDITORY CORTEX; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING;

ADOLESCENT; ADULT; AUDITORY CORTEX; BRAIN MAPPING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; SENSITIVITY AND SPECIFICITY; YOUNG ADULT;

EID: 77952305500     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.12.074     Document Type: Article

References (41)

1. Abdul-Kareem, I.A., Sluming, V., 2008. Heschl gyrus and its included primary auditory cortex: Structural MRI studies in healthy and diseased subjects. J. Magn. Reson. Imag. 28 (2), 287-299.
2. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38 (1), 95-113.
3. Ashburner, J., Friston, K.J., 2005. Unified segmentation. NeuroImage 26 (3), 839-851.
4. Balci, S.K., Golland, P., Shenton, M., Wells, W.M., 2007. Free-form B-spline deformation model for groupwise registration. Workshop on Statistical Registration, Medical Image Computing and Computer Assisted Intervention -MICCAI, pp. 23-30.
5. Chang, H.H., Zhuang, A.H., Valentino, D.J., Chu, W., 2009. Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47 (1), 122-135.
6. Christensen, G.E., Rabbitt, R.D., Miller, M.I., 1994. 3D brain mapping using a deformable neuroanatomy. Phys. Med. Biol. 39 (3), 609-618.
7. Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 (3), 162-173.
8. Davatzikos, C., 1996. Spatial normalization of 3D brain images using deformable models. Journal of Computer Assisted Tomography 20 (4), 656-665.
9. Dice, L., 1945. Measures of the amount of ecologic association between species. Ecology 26 (3), 297-302.
10. Evans, A., Marrett, S., Neelin, P., Collins, L., Wbrsley, K., Dai, W., Milot, S., Meyer, E., Bub, D., 1992. Anatomical mapping of functional activation in stereotactic coordinate space. NeuroImage 1 (1), 43-53.
11. Evans, A.C., Kamber, M., Collins, D.L., MacDonald, D., 1994. An MRI-based probabilistic atlas of neuroanatomy. Magnetic resonance scanning and epilepsy. In: Shorvon, S., Fish, D., Andermann, F., Bydder, G., Stefan, H. (Eds.), NATO ASI Series A, Life Sciences, vol. 264. Plenum Press, New York, pp. 263-274.
12. Evans, A.C., Collins, D.L., Holmes, C.J., 1996. Computational approaches to quantifying human neuroanatomical variability. In: Toga, A.W., John, C., Mazziotta, J.C. (Eds.), Brain Mapping: The Methods. Academic Press, San Diego, pp. 343-361.
13. Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M., 1999. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8 (4), 272-284.
14. Fox, P.T., Perlmutter, S., Raichle, M.E., 1985. A stereotactic method of anatomical localization for positron emission tomography. J. Compu. Assist. Tomogr. 9 (1), 141-153.
15. Geng, X., Christensen, G.E., Gu, H., Ross, T.J., Yang, Y., 2009. Implicit reference-based group-wise image registration and its application to structural and functional MRI. NeuroImage 47 (4), 1341-1351.
16. Höhne, K.H., Bomans, M., Riemer, M., Schubert, R., Tiede, U., Lierse, W., 1992. A volumebased anatomical atlas. IEEE Comput. Graph. Appl. 12 (4), 72-78
17. Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C., 1998. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22 (2), 324-333.
18. Jaccard, P., 1912. The distribution offlora in the alpine zone. New Phytologist 11 (2), 37-50.
19. Kikinis, R., Shenton, M.E., Iosifescu, D.V., McCarley, R.W., Saiviroonporn, P., Hokama, H.H., Robatino, A., Metcalf, D., Wible, C.G., Portas, C.M., Donnino, R.M., Jolesz, F.A., 1996. A digital brain atlas for surgical planning, model-driven segmentation, and teaching. IEEE Trans. Vis. Comput. Graph. 2 (3), 232-241.
20. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V., 2009. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46 (3), 786-802.
21. Leonard, C.M., Puranik, C., Kuldau, J.M., Lombardino, L.J., 1998. Normal variation in the frequency and location of human auditory cortex landmarks. Heschl's gyrus: where is it? Cereb. Cortex 8 (5), 397-406.
22. Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., Zilles, K., 2001. Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. NeuroImage 13 (4), 684-701.
23. MRIcron, 2009. Available from http://www.sph.sc.edu/comd/rorden/mricron/.
24. Nowinski, W.L., Belov, D., 2003. The cerefy neuroradiology atlas: a Talairach-Tournoux atlas-based tool for analysis of neuroimages available over the internet. NeuroImage 20 (1), 50-57.
25. Penhune, V.B., Zatorre,R.J.,MacDonald, J.D., Evans, A.C., 1996. Interhemispheric anatomical differences in human primary auditory cortex: probabilistic mapping and volume measurement from magnetic resonance scans. Cereb. Cortex 6 (5), 661-672.
26. Pohl, K., Fisher, J., Wells, W., Kikinis, R., Grimson, W., 2005. Shape based segmentation of anatomical structures in magnetic resonance images. IEEE International Conference on Computer Vision 3765, 489-498.
27. Rademacher, J., Galaburda, A.M., Kennedy, D.N., Filipek, P.A., Caviness, V.S., 1992. Human cerebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging. J. Cogn. Neurosci. 4 (4), 352-374.
28. Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.J., Zilles, K., 2001. Probabilistic mapping and volume measurement of human primary auditory cortex. NeuroImage 13 (4), 669-683.
29. Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W., 2008. Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39 (3), 1064-1080.
30. Shen, D., Davatzikos, C., 2002. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 22 (11), 1421-1439.
31. Thompson, P.M., Schwartz, C., Lin, R.T., Khan, A.A., Toga, A.W., 1996. Three-dimensional statistical analysis of sulcal variability in the human brain. J. Neurosci. 16 (13), 4261-4274.
32. Thompson, P.M., Woods, R.P., Mega, M.S., Toga, A.W., 2000. Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. Hum. Brain Mapp. 9 (2), 81-92.
33. tissue probability maps, I, March 2009. http://www.loni.ucla.edu/icbm/downloads/downloads_icbmprobabilistic.shtml.
34. Toga, A.W., Mazziotta, J.C., 2002. Brain Mapping: The Methods2nd Edition. Academic Press, New York. Toga, Brain Mapping.
35. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomical labelling of activations in spm using a macroscopic anatomical parcellation of the MNI MRI single subject brain. NeuroImage 15 (1), 273-289.
36. Warfield, S.K., Zou, K.H., Wells, W.M., 2006. Validation of image segmentation by estimating rater bias and variance. Medical Image Computing and Computer-Assisted Intervention - MICCAI 4191, 839-847.
37. Woods, R.P., 2000. Spatial Transformation Models. Handbook of Medical Imaging. Academic Press, Inc., pp. 465-490.
38. Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C., 1994. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13 (4), 716-724.
39. Zilles, K., Schleicher, A., Palomero-Gallagher, N., Amunts, K., 2002. Quantitative analysis of cyto- and receptorarchitecture of the human brain, Brain Mapping: The Methods, 2nd Edn., pp. 573-602.
40. Zou, K.H., Warfield, S.K., Bharatha, A., Tempany, C.M.C., Kaus, M.R., Haker, S.J., Wells, W.M., Jolesz, F.A., Kikinis, R., 2004. Statistical validation of image segmentation quality based on a spatial overlap index. Acad. Radiol. 11 (2), 178-189.
41. Zweig, M.H., Campbell, G., 1993. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39 (4), 561-577.