Groupwise multi-atlas segmentation of the spinal cord's internal structure

Medical Image Analysis

Volumn 18, Issue 3, 2014, Pages 460-471

  Asman, Andrew J ^a   Bryan, Frederick W ^a   Smith, Seth A ^b   Reich, Daniel S ^c   Landman, Bennett A ^a,b  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Cervical spinal cord segmentation; Groupwise registration; Multi atlas segmentation; Spinal cord internal structure

Indexed keywords

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPINAL CORD; SPINE DISEASE;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPINAL CORD; SPINAL DISEASES; SUBTRACTION TECHNIQUE;

EID: 84894115721     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.01.003     Document Type: Article

References (73)

1. Aljabar P., Heckemann R., Hammers A., Hajnal J., Rueckert D. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 2009, 46:726-738.
2. Amari S., Cichocki A., Yang H.H. A new learning algorithm for blind signal separation. Adv. Neural Inform. Process. Syst. 1996, 757-763.
3. Artaechevarria X., Muñoz-Barrutia A., Ortiz-de-Solorzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans. Med. Imag. 2009, 28:1266-1277.
4. Asman A., Landman B. Characterizing Spatially Varying Performance to Improve Multi-Atlas Multi-Label Segmentation. Information Processing in Medical Imaging (IPMI) 2011, vol. 6801. Springer, pp. 85-96.
5. Asman A., Landman B. Robust statistical label fusion through consensus level, labeler accuracy and truth estimation (COLLATE). IEEE Trans. Med. Imag. 2011, 30:1779-1794.
6. Asman A.J., Landman B.A. Formulating spatially varying performance in the statistical fusion framework. IEEE Trans. Med. Imag. 2012, 31:1326-1336.
7. Asman A.J., Landman B.A. Non-Local STAPLE: An Intensity-Driven Multi-Atlas Rater Model, Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2012, Springer, Nice, France, pp. 417-424.
8. Asman A.J., Landman B.A. Non-local statistical label fusion for multi-atlas segmentation. Med. Image Anal. 2012, 17:194-208.
9. Asman A.J., Smith S.A., Reich D.S., Landman B.A. Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion, Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2013, Springer, Nagoya, Japan.
10. Avants B., Epstein C., Grossman M., Gee J. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 2008, 12:26-41.
11. Avants, B.B., Tustison, N., Song, G., 2009. Advanced Normalization Tools (ANTS). Insight Journal.
12. Balci S.K., Golland P., Shenton M., Wells W.M. Free-form B-spline deformation model for groupwise registration. Med. Image Comput. Comput. Assist. Interv. 2007, 10:23-30.
13. Bede P., Bokde A., Elamin M., Byrne S., McLaughlin R.L., Jordan N., Hampel H., Gallagher L., Lynch C., Fagan A.J., Pender N., Hardiman O. Grey matter correlates of clinical variables in amyotrophic lateral sclerosis (ALS): a neuroimaging study of ALS motor phenotype heterogeneity and cortical focality. J. Neurol. Neurosurg. Psychiatry 2013, 84:766-773.
14. Bede P., Bokde A.L., Byrne S., Elamin M., Fagan A.J., Hardiman O. Spinal cord markers in ALS: diagnostic and biomarker considerations. Amyotroph. Lateral Scler. 2012, 13:407-415.
15. Bhatia K.K., Hajnal J., Hammers A., Rueckert D. Similarity metrics for groupwise non-rigid registration. Med. Image Comput. Comput. Assist. Interv. 2007, 10:544-552.
16. Cao Y., Yuan Y., Li X., Turkbey B., Choyke P.L., Yan P. Segmenting images by combining selected atlases on manifold. Med. Image Comput. Comput. Assist. Interv. 2011, 14:272-279.
17. Carballido-Gamio J., Belongie S.J., Majumdar S. Normalized cuts in 3-D for spinal MRI segmentation. IEEE Trans. Med. Imag. 2004, 23:36-44.
18. Cardoso M.J., Leung K., Modat M., Keihaninejad S., Cash D., Barnes J., Fox N.C., Ourselin S. STEPS: similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Med. Image Anal. 2013, 17:671-684.
19. Chen M., Carass A., Cuzzocreo J., Bazin P.-L., Reich D.S., Prince J.L. Topology preserving automatic segmentation of the spinal cord in magnetic resonance images. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011, 1737-1740. IEEE.
20. Commowick O., Akhondi-Asl A., Warfield S.K. Estimating a reference standard segmentation with spatially varying performance parameters: local MAP STAPLE. IEEE Trans. Med. Imag. 2012, 31:1593-1606.
21. Commowick O., Warfield S. Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE. Med. Image Comput. Comput.-Assist. Interv. - MICCAI 2010, 2010:25-32.
22. Commowick O., Wiest-Daesslé N., Prima S. Automated diffeomorphic registration of anatomical structures with rigid parts: application to dynamic cervical MRI. Med. Image Comput. Comput.-Assist. Interv. - MICCAI 2012, 2012:163-170.
23. Cootes T.F., Edwards G.J., Taylor C.J. Active appearance models. IEEE Trans. Pattern Anal. Machine Intell. 2001, 23:681-685.
24. Cootes T.F., Taylor C.J., Cooper D.H., Graham J. Active shape models-their training and application. Comput. Vis. Image Understand. 1995, 61:38-59.
25. Coupé P., Manjķn J.V., Fonov V., Pruessner J., Robles M., Collins D.L. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 2011, 54:940-954.
26. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B (Methodol.) 1977, 1-38.
27. Depa, M., Holmvang, G., Schmidt, E.J., Golland, P., Sabuncu, M.R., 2011. Towards efficient label fusion by pre-alignment of training data. In: Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, pp. 38-46.
28. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
29. Dietz V., Curt A. Neurological aspects of spinal-cord repair: promises and challenges. Lancet Neurol. 2006, 5:688-694.
30. Farrell J.A., Smith S.A., Gordon-Lipkin E.M., Reich D.S., Calabresi P.A., van Zijl P.C. High b-value q-space diffusion-weighted MRI of the human cervical spinal cord in vivo: feasibility and application to multiple sclerosis. Magnet. Reson. Med. 2008, 59:1079-1089. official journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine.
31. Gilmore C.P., Bö L., Owens T., Lowe J., Esiri M.M., Evangelou N. Spinal cord gray matter demyelination in multiple sclerosis-a novel pattern of residual plaque morphology. Brain Pathol. 2006, 16:202-208.
32. 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.
33. Hinks R.S., Quencer R.M. Motion artifacts in brain and spine MR. Radiol. Clin. North Am. 1988, 26:737-753.
34. Horsfield M.A., Sala S., Neema M., Absinta M., Bakshi A., Sormani M.P., Rocca M.A., Bakshi R., Filippi M. Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage 2010, 50:446-455.
35. Huang S.H., Chu Y.H., Lai S.H., Novak C.L. Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Trans. Med. Imag. 2009, 28:1595-1605.
36. Huttenlocher D.P., Klanderman G.A., Rucklidge W.J. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Machine Intell. 1993, 15:850-863.
37. Isgum I., Staring M., Rutten A., Prokop M., Viergever M.A., van Ginneken B. Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imag. 2009, 28:1000-1010.
38. Jarius S., Wildemann B. AQP4 antibodies in neuromyelitis optica: diagnostic and pathogenetic relevance. Nature Rev. Neurol. 2010, 6:383-392.
39. Jenkinson M., Smith S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 2001, 5:143-156.
40. Jia H., Yap P.-T., Shen D. Iterative multi-atlas-based multi-image segmentation with tree-based registration. Neuroimage 2012, 59:422-430.
41. Jolliffe I.T. Principal Component Analysis 1986, Springer-Verlag, New York.
42. Kaminsky J., Klinge P., Rodt T., Bokemeyer M., Luedemann W., Samii M. Specially adapted interactive tools for an improved 3D-segmentation of the spine. Comput. Med. Imag. Graph. 2004, 28:119-127. the official journal of the Computerized Medical Imaging Society.
43. Karpova A., Arun R., Davis A.M., Kulkarni A.V., Mikulis D.J., Sooyong C., Rabin D., Craciunas S., Smith S.R., Hansen M.A., George J., Fehlings M.G. Reliability of quantitative magnetic resonance imaging methods in the assessment of spinal canal stenosis and cord compression in cervical myelopathy. Spine 2013, 38:245-252.
44. Klein A., Andersson J., Ardekani B.A., Ashburner J., Avants B., Chiang M.C., Christensen G.E., Collins D.L., Gee J., Hellier P. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 2009, 46:786-802.
45. Lagarias J.C., Reeds J.A., Wright M.H., Wright P.E. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. 1998, 9:112-147.
46. Landman, B.A., Ribbens, A., Lucas, B., Davatzikos, C., Avants, B., Ledig, C., Ma, D., Rueckert, D., Vandermeulen, D., Maes, F., Erus, G., Wang, J., Holmes, H., Wang, H., Doshi, J., Kornegay, J., Manjon, J., Hammers, A., Akhondi-Asl, A., Asman, A.J., Warfield, S.K., 2012. MICCAI 2012 Workshop on Multi-Atlas Labeling. CreateSpace Independent Publishing Platform.
47. Langerak T.R., van der Heide U.A., Kotte A.N.T.J., Viergever M.A., van Vulpen M., Pluim J.P.W. Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med. Imag. 2010, 29:2000-2008.
48. Ledig C., Wolz R., Aljabar P., Lotjonen J., Heckemann R.A., Hammers A., Rueckert D. Multi-class brain segmentation using atlas propagation and EM-based refinement. 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012, 896-899. IEEE.
49. Lucas B.C., Bogovic J.A., Carass A., Bazin P.-L., Prince J.L., Pham D.L., Landman B.A. The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 2010, 8:5-17.
50. Ma J., Lu L., Zhan Y., Zhou X., Salganicoff M., Krishnan A. Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Med. Image Comput. Comput. Assist. Interv. 2010, 13:19-27.
51. McAuliffe M.J., Lalonde F.M., McGarry D., Gandler W., Csaky K., Trus B.L. Medical image processing, analysis and visualization in clinical research. Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems, 2001 (CBMS 2001) 2001, 381-386. IEEE.
52. McIntosh C., Hamarneh G. Spinal crawlers: deformable organisms for spinal cord segmentation and analysis. Med. Image Comput. Comput. Assist. Interv. 2006, 9:808-815.
53. Mikulis D.J., Wood M.L., Zerdoner O.A., Poncelet B.P. Oscillatory motion of the normal cervical spinal cord. Radiology 1994, 192:117-121.
54. Ourselin S., Roche A., Subsol G., Pennec X., Ayache N. Reconstructing a 3D structure from serial histological sections. Image Vis. Comput. 2001, 19:25-31.
55. Ozturk A., Aygun N., Smith S.A., Caffo B., Calabresi P.A., Reich D.S. Axial 3D gradient-echo imaging for improved multiple sclerosis lesion detection in the cervical spinal cord at 3T. Neuroradiology 2013, 55:431-439.
56. Pitiot A., Bardinet E., Thompson P.M., Malandain G. Piecewise affine registration of biological images for volume reconstruction. Med. Image Anal. 2006, 10:465-483.
57. 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.
58. Rohlfing T., Russakoff D.B., Maurer C.R. Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans. Med. Imag. 2004, 23:983-994.
59. Sabuncu M.R., Yeo B.T.T., Van Leemput K., Fischl B., Golland P. A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imag. 2010, 29:1714-1729.
60. Smith S.A., Edden R.A., Farrell J.A., Barker P.B., Van Zijl P.C. Measurement of T1 and T2 in the cervical spinal cord at 3 tesla. Magnet. Reson. Med. 2008, 60:213-219. official journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine.
61. Smith S.A., Jones C.K., Gifford A., Belegu V., Chodkowski B., Farrell J.A., Landman B.A., Reich D.S., Calabresi P.A., McDonald J.W., van Zijl P.C. Reproducibility of tract-specific magnetization transfer and diffusion tensor imaging in the cervical spinal cord at 3 tesla. NMR Biomed. 2010, 23:207-217.
62. Turk M., Pentland A. Eigenfaces for recognition. J. Cogn. Neurosci. 1991, 3:71-86.
63. 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.
64. Wang H., Das S.R., Suh J.W., Altinay M., Pluta J., Craige C., Avants B., Yushkevich P.A. 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.
65. Wang H., Suh J.W., Das S.R., Pluta J., Craige C., Yushkevich P.A. Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Machine Intell. 2012, 35:611-623.
66. Warfield S.K., Zou K.H., Wells W.M. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 2004, 23:903-921.
67. Weisenfeld N.I., Warfield S.K. Automatic segmentation of newborn brain MRI. Neuroimage 2009, 47:564-572.
68. Wingerchuk D.M., Lennon V.A., Lucchinetti C.F., Pittock S.J., Weinshenker B.G. The spectrum of neuromyelitis optica. Lancet Neurol. 2007, 6:805-815.
69. Wolz R., Aljabar P., Hajnal J.V., Hammers A., Rueckert D. LEAP: learning embeddings for atlas propagation. Neuroimage 2010, 49:1316-1325.
70. Wolz R., Chu C., Misawa K., Mori K., Rueckert D. Multi-organ Abdominal CT segmentation using hierarchically weighted subject-specific atlases. Med. Image Comput. Comput.-Assist. Interv. - MICCAI 2012, 2012:10-17.
71. Yiannakas M., Kearney H., Samson R., Chard D., Ciccarelli O., Miller D., Wheeler-Kingshott C. Feasibility of grey matter and white matter segmentation of the upper cervical cord in vivo: a pilot study with application to magnetisation transfer measurements. Neuroimage 2012, 63:1054-1059.
72. Zackowski K.M., Smith S.A., Reich D.S., Gordon-Lipkin E., Chodkowski B.A., Sambandan D.R., Shteyman M., Bastian A.J., van Zijl P.C., Calabresi P.A. Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord. Brain: J. Neurol. 2009, 132:1200-1209.
73. Zhang J. The mean field theory in EM procedures for Markov random fields. IEEE Trans. Signal Process. 1992, 40:2570-2583.