Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures

NeuroImage

Volumn 39, Issue 1, 2008, Pages 238-247

  Powell, Stephanie ^a   Magnotta, Vincent A ^a   Johnson, Hans ^a   Jammalamadaka, Vamsi K ^b   Pierson, Ronald ^a   Andreasen, Nancy C ^a  

^a UNIVERSITY OF IOWA   (United States)

^b Vital Images   (United States)

Author keywords

Artificial neural networks; Brain segmentation; MRI; Registration based segmentation; Support vector machine

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BRAIN CORTEX; CEREBELLUM; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBELLUM; CEREBRAL CORTEX; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 36048967893     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2007.05.063     Document Type: Article

References (37)

1. Akselrod-Ballin A., Galun M., Gomori M.J., Basri R., and Brandt A. Atlas guided identification of brain structures by combining 3D segmentation and SVM classification. Med. Image Comput. Comput. Assist Interv. Int. Conf. Med. Image Comput. Comput. Assist Interv. 9 (2006) 209-216
2. Andreasen N.C., Cohen G., Harris T., Cizadlo T., Parkkinen J., Rezai K., and Swayze II V.W. Image processing for the study of brain structure and function: problems and programs. J. Neuropsychiatry Clin. Neurosci. 4 2 (1992) 125-133
3. Andreasen N.C., Cizadlo T., Harris G., Swayze II V., O'Leary D.S., Cohen G., Ehrhardt J., and Yuh W.T. Voxel processing techniques for the antemortem study of neuroanatomy and neuropathology using magnetic resonance imaging. J. Neuropsychiatry Clin. Neurosci. 5 2 (1993) 121-130
4. Bookstein F.L. Thin-plate splines and the atlas problem for biomedical images. Int. Conf. Inf. Proc. Med. Imag. 12 (1991) 326-342
5. Cagnoni S., Coppini G., Rucci M., Caramella D., and Valli G. Neural network segmentation of magnetic resonance spin echo images of the brain. J. Biomed. Eng. 15 5 (1993) 355-362
6. Clarke L.P., Velthuizen R.P., Phuphanich S., Schellenberg J.D., Arrington J.A., and Silbiger M. MRI: stability of three supervised segmentation techniques. Magn. Reson. Imaging 11 1 (1993) 95-106
7. Dickson S., Thomas B.T., and Goddard P. Using neural networks to automatically detect brain tumours in MR images. Int. J. Neural Syst. 8 1 (1997) 91-99
8. Fischl B., Salat D.H., Busa E., Albert M., Dieterich M., Haselgrove C., van der Kouwe A., Killiany R., Kennedy D., Klaveness S., Montillo A., Makris N., Rosen B., and Dale A.M. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33 3 (2002) 341-355
9. Glass J.O., Ji Q., Glas L.S., and Reddick W.E. Prediction of total cerebral tissue volumes in normal appearing brain from sub-sampled segmentation volumes. Magn. Reson. Imaging 21 9 (2003) 977-982
10. Grevera G.J., and Udupa J.K. Shape-based interpolation of multidimensional grey-level images. IEEE Trans. Med. Imaging 15 (1996) 881-892
11. Harris G., Andreasen N.C., Cizadlo T., Bailey J.M., Bockholt H.J., Magnotta V.A., and Arndt S. Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection. J. Comput. Assist. Tomogr. 23 1 (1999) 144-154
12. Holmes C.J., Hoge R., Collins L., Woods R., Toga A.W., and Evans A.C. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22 (1998) 324-333
13. Iosifescu D.V., Shenton M.E., Warfield S.K., Kikinis R., Dengler J., Jolesz F.A., and McCarley R.W. An automated registration algorithm for measuring MRI subcortical brain structures. NeuroImage 6 1 (1997) 13-25
14. Lin J.S., Cheng K.S., and Mao C.W. Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network. Int. J. Biomed. Comput. 42 3 (1996) 205-214
15. Lin J.S., Cheng K.S., and Mao C.W. Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network. Comput. Biomed. Res. 29 4 (1996) 314-326
16. Magnotta V.A., Heckel D., Andreasen N.C., Cizadlo T., Corson P.W., Ehrhardt J.C., and Yuh W.T. Measurement of brain structures with artificial neural networks: two- and three-dimensional applications. Radiology 211 3 (1999) 781-790
17. Magnotta V.A., Harris G., Andreasen N.C., O'Leary D.S., Yuh W.T., and Heckel D. Structural MR image processing using the BRAINS2 toolbox. Comput. Med. Imaging Graph 26 4 (2002) 251-264
18. Mulhern R.K., Reddick W.E., Palmer S.L., Glass J.O., Elkin T.D., Kun L.E., Taylor J., Langston J., and Gajjar A. Neurocognitive deficits in medulloblastoma survivors and white matter loss. Ann. Neurol. 46 6 (1999) 834-841
19. Osuna E.E., Freund R., Girosi F., 1996. Support Vector Machines: Training and Applications. A.I. Memo, MIT, A. I. Lab.
20. Pantel J., O'Leary D.S., Cretsinger K., Bockholt H.J., Keefe H., Magnotta V.A., and Andreasen N.C. A new method for the in vivo volumetric measurement of the human hippocampus with high neuroanatomical accuracy. Hippocampus 10 6 (2000) 752-758
21. Perez de Alejo R., Ruiz-Cabello J., Cortijo M., Rodriguez I., Echave I., Regadera J., Arrazola J., Aviles P., Barreiro P., Gargallo D., and Grana M. Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks. Magn. Reson. Imaging 21 8 (2003) 901-912
22. Pierson R., Corson P.W., Sears L.L., Alicata D., Magnotta V., Oleary D., and Andreasen N.C. Manual and semiautomated measurement of cerebellar subregions on MR images. NeuroImage 17 1 (2002) 61-76
23. Piraino D.W., Amartur S.C., Richmond B.J., Schils J.P., Thome J.M., and Weber P.B. Segmentation of magnetic resonance images using an artificial neural network. Proc. Annu. Symp. Comput. Appl. Med. (1991) 470-472
24. Prakash B., Gupta V., Bilello M., Beauchamp N.J., and Nowinski N.L. Identification, Segmentation, and image property study of acute infarcts in diffusion-weighted images by using a probabilistic neural network and adaptive Gaussian mixture model. Acad. Radiol. 13 (2006) 1474-1484
25. Quddus A., Fieguth P., and Basir O. Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images. Engineering in Medicine and Biology Society. IEEE-EMBS 2005. 27th Annual International Conference, Shanghai, China (2006) 463-466
26. Reddick W.E., Glass J.O., Cook E.N., Elkin T.D., and Deaton R.J. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans. Med. Imag. 16 6 (1997) 911-918
27. Shen S., Sandham W., Granat M., and Sterr A. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inf. Technol. Biomed. 9 3 (2005) 459-467
28. Song T., Gasparovic C., Andreasen N., Bockholt J., Jamshidi M., Lee R.R., and Huang M. A hybrid tissue segmentation approach for brain MR images. Med. Biol. Eng. Comput. 44 3 (2006) 242-249
29. Spinks R., Magnotta V.A., Andreasen N.C., Albright K.C., Ziebell S., Nopoulos P., and Cassell M. Manual and automated measurement of the whole thalamus and mediodorsal nucleus using magnetic resonance imaging. NeuroImage 17 2 (2002) 631-642
30. Talairach J., and Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain (1988), Thieme, Stuttgart, Germany
31. Thirion J.P. Image matching as a diffusion process: an analogy with Maxwell's demons. Med. Image Anal. 2 3 (1998) 243-260
32. Valdes-Cristerna R., Medina-Banuelos V., and Yanez-Suarez O. Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation. IEEE Trans. Biomed. Eng. 51 3 (2004) 459-470
33. Wagenknecht G., Kaiser H.J., Buell U., and Sabri O. MRI-based individual 3D region-of-interest atlases of the human brain: a new method for analyzing functional data. Methods Inf. Med. 43 4 (2004) 383-390
34. Wismuller A., Vietze F., Behrends J., Meyer-Baese A., Reiser M., and Ritter H. Fully automated biomedical image segmentation by self-organized model adaptation. IEEE Trans. Med. Imag. 25 1 (2006) 62-73
35. Woods R.P., Grafton S.T., Holmes C.J., Cherry S.R., and Mazziotta J.C. Automated image registration: II. Intersubject validation of linear and nonlinear models. J. Comput. Assist. Tomogr. 22 1 (1998) 153-165
36. Wu M., Carmichael O., Lopez-Garcia P., Carter C.S., and Aizenstein H.J. Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images. Hum. Brain Mapp. 27 (2006) 747-754
37. Zhou J., Chan K.L., Chong V.F.H., and Krishnan S.M. Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine. Engineering in Medicine and Biology Society. IEEE-EMBS 2005. 27th Annual International, 2005, Conference, Shanghai, China (2005) 6411-6414.