Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images

Journal of Neurosurgery: Pediatrics

Volumn 15, Issue 2, 2015, Pages 113-124

  Mandell, Jason G ^a,b   Langelaan, Jack W ^a   Webb, Andrew G ^c   Schiff, Steven J ^a,b  

^a The Pennsylvania State University   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Brain growth; Cerebrospinal fluid; Hydrocephalus; Particle filter; Segmentation; Technique

Indexed keywords

ALGORITHM; ARTICLE; BRAIN ANALYSIS; BRAIN GROWTH; BRAIN SIZE; CEREBROSPINAL FLUID; CHILD; CLINICAL ARTICLE; COGNITIVE DEVELOPMENT; COMPUTER ASSISTED TOMOGRAPHY; GROWTH CURVE; HUMAN; HYDROCEPHALUS; IMAGE ANALYSIS; NATIONAL HEALTH ORGANIZATION; NEUROIMAGING; NEUROSURGERY; NUCLEAR MAGNETIC RESONANCE IMAGING; PARTICLE FILTER SEGMENTATION; PRIORITY JOURNAL; SIMULATION; ADOLESCENT; BRAIN; COGNITION; CONE BEAM COMPUTED TOMOGRAPHY; FEMALE; FRONTAL LOBE; INFANT; MALE; OCCIPITAL LOBE; ORGAN SIZE; PATHOLOGY; PRESCHOOL CHILD; PROCEDURES; PSYCHOLOGY; RADIOGRAPHY;

ADOLESCENT; BRAIN; CEREBROSPINAL FLUID; CHILD; CHILD, PRESCHOOL; COGNITION; CONE-BEAM COMPUTED TOMOGRAPHY; FEMALE; FRONTAL LOBE; HUMANS; HYDROCEPHALUS; INFANT; MAGNETIC RESONANCE IMAGING; MALE; NEUROSURGICAL PROCEDURES; OCCIPITAL LOBE; ORGAN SIZE;

EID: 84964696335     PISSN: 19330707     EISSN: 19330715     Source Type: Journal    
DOI: 10.3171/2014.9.PEDS12426     Document Type: Article

References (34)

1. Abolmaesumi P, Sirouspour MR: An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images. IEEE Trans Med Imaging 23:772-784, 2004
2. Almli CR, Rivkin MJ, McKinstry RC: The NIH MRI study of normal brain development (Objective-2): newborns, infants, toddlers, and preschoolers. Neuroimage 35:308-325, 2007
3. Bankman IN: Handbook of Medical Image Processing and Analysis, ed 2. Burlington, MA: Academic Press, 2009
4. Bazin PL, Pham DL: Topology-preserving tissue classification of magnetic resonance brain images. IEEE Trans Med Imaging 26:487-496, 2007
5. Briellmann RS, Pell GS, Wellard RM, Mitchell LA, Abbott DF, Jackson GD: MR imaging of epilepsy: state of the art at 1.5 T and potential of 3 T. Epileptic Disord 5:3-20, 2003
6. Cocosco CA, Kollokian V, Kwan RKS, Evans AC: Brain- Web: online interface to a 3D MRI simulated brain database. Proceedings of 3rd International Conference on Functional Mapping of the Human Brain, Copenhagen, May 1997. NeuroImage 5:S425, 1997 (Abstract)
7. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, et al: Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17:463-468, 1998
8. Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, et al: Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216:672-682, 2000
9. Deans SR: The Radon Transform and Some of Its Applications. New York: John Wiley & Sons, 1983
10. Dice LR: Measures of the amount of ecologic association between species. Ecology 26:297-302, 1945
11. Drew PJ, Blinder P, Cauwenberghs G, Shih AY, Kleinfeld D: Rapid determination of particle velocity from space-time images using the Radon transform. J Comput Neurosci 29:5-11, 2010
12. Flury B: A First Course in Multivariate Statistics. New York: Springer, 1997
13. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, et al: Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 2:861-863, 1999
14. Gould SJ: The Mismeasure of Man. New York: Norton, 1981
15. Hüppi PS, Warfield S, Kikinis R, Barnes PD, Zientara GP, Jolesz FA, et al: Quantitative magnetic resonance imaging of brain development in premature and mature newborns. Ann Neurol 43:224-235, 1998
16. Mandell JG, Hill KL, Nguyen DTD, Moser KW, Harbaugh RE, McInerney J, et al: Volumetric brain analysis in neurosurgery: Part 3. Volumetric CT analysis as a predictor of seizure outcome following temporal lobectomy. J Neurosurg Pediatr [epub ahead of print November 28, 2014. DOI: 10.3171/2014.9.PEDS12428]
17. Mandell JG, Kulkarni AV, Warf BC, Schiff SJ: Volumetric brain analysis in neurosurgery: Part 2. Brain and CSF volumes discriminate neurocognitive outcomes in hydrocephalus. J Neurosurg Pediatr [epub ahead of print November 28, 2014. DOI: 10.3171/2014.9.PEDS12427]
18. Mandell JG, Neuberger T, Drapaca CS, Webb AG, Schiff SJ: The dynamics of brain and cerebrospinal fluid growth in normal versus hydrocephalic mice. Laboratory investigation. J Neurosurg Pediatr 6:1-10, 2010
19. Matsumae M, Kikinis R, Mórocz I, Lorenzo AV, Albert MS, Black PM, et al: Intracranial compartment volumes in patients with enlarged ventricles assessed by magnetic resonance-based image processing. J Neurosurg 84:972-981, 1996
20. Mortensen EN, Barrett WA: Interactive segmentation with intelligent scissors. Graph Models Image Proc 60:349-384, 1998
21. Pham DL, Xu C, Prince JL: Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315-337, 2000
22. Reiss AL, Abrams MT, Singer HS, Ross JL, Denckla MB: Brain development, gender and IQ in children. A volumetric imaging study. Brain 119:1763-1774, 1996
23. Rencher AC: Methods of Multivariate Analysis, ed 2. New York: John Wiley & Sons, 2002
24. Roche AF, Mukherjee D, Guo SM, Moore WM: Head circumference reference data: birth to 18 years. Pediatrics 79:706-712, 1987
25. Schiff SJ: Neural Control Engineering: The Emerging Intersection between Control Theory and Neuroscience. Cambridge, MA: Massachusetts Institute of Technology Press, 2012
26. Shim H, Kwon D, Yun ID, Lee SU: Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: particle filtering. Comput Methods Programs Biomed 84:135-145, 2006
27. Simon D: Optimal State Estimation. Hoboken, NJ: John Wiley & Sons, Inc, 2006
28. Sonka M, Hlavac V, Boyle R: Image Processing, Analysis, and Machine Vision, ed 3. Stamford, CT: Cengage Learning, 2007
29. Thrun S, Burgard W, Fox D: Probabilistic Robotics. Cambridge, MA: Massachusetts Institute of Technology Press, 2006
30. Vaughan JT, Garwood M, Collins CM, Liu W, DelaBarre L, Adriany G, et al: 7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images. Magn Reson Med 46:24-30, 2001
31. Waber DP, De Moor C, Forbes PW, Almli CR, Botteron KN, Leonard G, et al: The NIH MRI study of normal brain development: performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. J Int Neuropsychol Soc 13:729-746, 2007
32. Warf B, Ondoma S, Kulkarni A, Donnelly R, Ampeire M, Akona J, et al: Neurocognitive outcome and ventricular volume in children with myelomeningocele treated for hydrocephalus in Uganda. Clinical article. J Neurosurg Pediatr 4:564-570, 2009
33. Withey DJ, Pedrycz W, Koles ZJ: Dynamic edge tracing: boundary identification in medical images. Comput Vis Image Underst 113:1039-1052, 2009
34. Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, et al: Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178-189, 2004