Automatic segmentation of MR brain images of preterm infants using supervised classification

NeuroImage

Volumn 118, Issue , 2015, Pages 628-641

  Moeskops, Pim ^a,b   Benders, Manon J N L ^b   Chiţă, Sabina M ^a   Kersbergen, Karina J ^b   Groenendaal, Floris ^b   de Vries, Linda S ^b   Viergever, Max A ^a   Išgum, Ivana ^a  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^b UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

Author keywords

Automatic brain segmentation; MRI; Preterm neonatal brain; Supervised voxel classification

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATIC SEGMENTATION; BRAIN CORTEX; BRAIN SIZE; CEREBROSPINAL FLUID; CONTROLLED STUDY; CORRELATION COEFFICIENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; DISEASE SEVERITY; EXTRACEREBRAL SPACE; GRAY MATTER; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; INFANT; MATHEMATICAL ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PATHOPHYSIOLOGY; POSTMENSTRUAL AGE; PREMATURITY; PRIORITY JOURNAL; PROBABILITY; SEQUENTIAL FORWARD FLOATING SELECTION; STANDARD; TISSUE SPECIFICITY; TRAINING; WHITE MATTER; BRAIN; COMPUTER ASSISTED DIAGNOSIS; EMBRYOLOGY; GROWTH, DEVELOPMENT AND AGING; NEUROIMAGING; NEWBORN; PROCEDURES;

ALGORITHMS; BRAIN; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFANT, NEWBORN; INFANT, PREMATURE; MAGNETIC RESONANCE IMAGING; NEUROIMAGING;

EID: 84940439646     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.06.007     Document Type: Article

References (38)

1. Anbeek P., Išgum I., van Kooij B.J., Mol C.P., Kersbergen K.J., Groenendaal F., Viergever M.A., de Vries L.S., Benders M.J. Automatic segmentation of eight tissue classes in neonatal brain MRI. PLoS One 2013, 8(12):e81895.
2. Awate S.P., Yushkevich P.A., Song Z., Licht D.J., Gee J.C. Cerebral cortical folding analysis with multivariate modeling and testing: studies on gender differences and neonatal development. NeuroImage 2010, 53(2):450-459.
3. Cardoso M.J., Melbourne A., Kendall G.S., Modat M., Robertson N.J., Marlow N., Ourselin S. AdaPT: an adaptive preterm segmentation algorithm for neonatal brain MRI. NeuroImage 2013, 65:97-108.
4. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2(3):27.
5. Chiţă S., Benders M.J., Moeskops P., Kersbergen K.J., Viergever M.A., Išgum I. Automatic segmentation of the preterm neonatal brain with MRI using supervised classification. SPIE Medical Imaging 2013, International Society for Optics and Photonics, (86693X-86693X).
6. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20(3):273-297.
7. Dubois J., Benders M., Borradori-Tolsa C., Cachia A., Lazeyras F., Ha-Vinh Leuchter R., Sizonenko S.V., Warfield S.K., Mangin J.F., Hüppi P.S. Primary cortical folding in the human newborn: an early marker of later functional development. Brain 2008, 131:2028-2041.
8. Dubois J., Benders M., Cachia A., Lazeyras F., Leuchter R.H.V., Sizonenko S.V., Borradori-Tolsa C., Mangin J., Hüppi P.S. Mapping the early cortical folding process in the preterm newborn brain. Cereb. Cortex 2008, 18(6):1444-1454.
9. Gholipour A., Estroff J.A., Barnewolt C.E., Connolly S.A., Warfield S.K. Fetal brain volumetry through MRI volumetric reconstruction and segmentation. Int. J. Comput. Assist. Radiol. Surg. 2011, 6(3):329-339.
10. Gui L., Lisowski R., Faundez T., Hüppi P.S., Lazeyras F., Kocher M. Morphology-driven automatic segmentation of MR images of the neonatal brain. Med. Image Anal. 2012, 16(8):1565-1579.
11. Habas P.A., Kim K., Rousseau F., Glenn O.A., Barkovich A.J., Studholme C. Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses. Hum. Brain Mapp. 2010, 31(9):1348-1358.
12. Hill J., Dierker D., Neil J., Inder T., Knutsen A., Harwell J., Coalson T., Van Essen D. A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J. Neurosci. 2010, 30(6):2268-2276.
13. Huttenlocher D., Klanderman G., Rucklidge W. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 1993, 15:850-863.
14. Išgum I., Benders M.J., Avants B., Cardoso M.J., Counsell S.J., Gomez E.F., Gui L., Hüppi P., Kersbergen K.J., A., et al. Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge. Med. Image Anal. 2015, 20(1):135-151.
15. Jain A.K., Zongker D. Feature selection: evaluation, application and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19:153-158.
16. Kuklisova-Murgasova M., Aljabar P., Srinivasan L., Counsell S.J., Doria V., Serag A., Gousias I.S., Boardman J.P., Rutherford M.A., Edwards A.D., Hajnal J.V., Rueckert D. A dynamic 4D probabilistic atlas of the developing brain. NeuroImage 2011, 54(4):2750-2763.
17. Lin H.T., Lin C.J., Weng R.C. A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 2007, 68:267-276.
18. Makropoulos A., Gousias I., Ledig C., Aljabar P., Serag A., Hajnal J., Edwards A.D., Counsell S., Rueckert D. Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 2014, 33(9):1818-1831.
19. Moeskops P., Benders M.J., Pearlman P.C., Kersbergen K.J., Leemans A., Viergever M.A., Išgum I. Assessment of quantitative cortical biomarkers in the developing brain of preterm infants. SPIE Medical Imaging 2013, International Society for Optics and Photonics, (867011-867011).
20. Moeskops P., Benders M.J.N.L., Kersbergen K.J., Groenendaal F., de Vries L.S., Viergever M.A., Išgum I. Development of cortical morphology evaluated with longitudinal MR brain images of preterm infants. PLOS ONE 2015, (in press). 10.1371/journal.pone.0131552.
21. Pienaar R., Fischl B., Caviness V., Makris N., Grant P.E. A methodology for analyzing curvature in the developing brain from preterm to adult. Int. J. Imaging Syst. Technol. 2008, 18(1):42-68.
22. Platt J.C. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers 1999, 61-74. MIT Press.
23. Prastawa M., Gilmore J.H., Lin W., Gerig G. Automatic segmentation of MR images of the developing newborn brain. Med. Image Anal. 2005, 9(5):457-466.
24. Pudil P., Novovicova J., Kittler J. Floating search methods in feature selection. Pattern Recogn. Lett. 1994, 15(11):1119-1125.
25. Rathbone R., Counsell S.J., Kapellou O., Dyet L., Kennea N., Hajnal J., Allsop J.M., Cowan F., Edwards A.D. Perinatal cortical growth and childhood neurocognitive abilities. Neurology 2011, 77(16):1510-1517.
26. Rodriguez-Carranza C.E., Mukherjee P., Vigneron D., Barkovich J., Studholme C. A framework for in vivo quantification of regional brain folding in premature neonates. NeuroImage 2008, 41(2):462-478.
27. Saigal S., Doyle L.W. An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet 2008, 371:261-269.
28. Serag A., Aljabar P., Ball G., Counsell S.J., Boardman J.P., Rutherford M.A., Edwards A.D., Hajnal J.V., Rueckert D. Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. NeuroImage 2012, 59(3):2255-2265.
29. Shi F., Fan Y., Tang S., Gilmore J.H., Lin W., Shen D. Neonatal brain image segmentation in longitudinal MRI studies. NeuroImage 2010, 49:391-400.
30. Smith S.M. Fast robust automated brain extraction. Hum. Brain Mapp. 2002, 17(3):143-155.
31. Song Z., Awate S.P., Licht D.J., Gee J.C. Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors. Medical Image Computing and Computer-Assisted Intervention 2007, 883-890. Springer.
32. Srhoj-Egekher V., Benders M.J., Viergever M.A., Išgum I. Automatic neonatal brain tissue segmentation with MRI. SPIE Medical Imaging, organizationInternational Society for Optics and Photonics 2013, (86693X-86693X).
33. 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(3):968-985.
34. Wang L., Shi F., Li G., Gao Y., Lin W., Gilmore J.H., Shen D. Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 2014, 84:141-158.
35. Weisenfeld N.I., Warfield S.K. Automatic segmentation of newborn brain MRI. NeuroImage 2009, 47(2):564-572.
36. Wright R., Vatansever D., Kyriakopoulou V., Ledig C., Wolz R., Serag A., Rueckert D., Rutherford M.A., Hajnal J.V., Aljabar P. Age dependent fetal MR segmentation using manual and automated approaches. MICCAI workshop on Perinatal and Paediatric Imaging 2012, 97-104.
37. Xue H., Srinivasan L., Jiang S., Rutherford M., Edwards A.D., Rueckert D., Hajnal J.V. Automatic segmentation and reconstruction of the cortex from neonatal MRI. NeuroImage 2007, 38(3):461-477.
38. Zou K.H., Warfield S.K., Bharatha A., Tempany C., Kaus M.R., Haker S.J., Wells W.M., Jolesz F.A., Kikinis R. Statistical validation of image segmentation quality based on a spatial overlap index. Acad. Radiol. 2004, 11(2):178-189.