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Study of the Prognostic Relevance of Longitudinal Brain Atrophy in Post-traumatic Diffuse Axonal Injury Using Graph-Based MRI Segmentation Techniques

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Computational Modeling of Objects Presented in Images

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 15))

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Abstract

Diffuse axonal injury is a kind of brain lesion on a microscopic level produced by the mechanism or rapid acceleration-deceleration of the head. It is characterized by a widespread damage throughout the brain with no focalization, but with prevalent involvement of some areas; it implies a state of coma of sudden onset followed by a recovery the entity of which may vary according to the amount of sustained damage. This traumatic pattern differs from focal lesion because it does not result in focal neurological deficit related to a particular brain area, but it interferes with more sophisticated functions relying upon brain networks, being damaged from a diffuse white matter shearing. For patients who recover from unconsciousness, disability is characterized by cognitive impairments with domains of memory and attention being mainly affected. When the patient is in coma clinicians has no proper instrument to predict the clinical evolution except the qualitative evaluation of lesions depicted on MRI scan. Delayed brain atrophy, measured on seriated MRI volumetric scans may represents an useful biomarker to predict the prognosis of these patients because data coming from recent observations show relevant correlations between the amount of WM atrophy and prognosis assessed by neuropsychological tests. Here we present a new graph-based method for MRI brain segmentation, and its application to our problem of WM atrophy estimation for prognostic inference.

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References

  1. Aboutanos GB, Dawant BM (1997) Automatic brain segmentation and validation: image-based versus atlas-based deformable modelsn. In: Proceedings of the SPIE-medical imaging, vol 3034. pp 299–310

    Google Scholar 

  2. Atkins MS, Mackiewich B, Whittall K (1998) Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 17(1): 98–107

    Google Scholar 

  3. Balafar M, Ramli A, Saripan M, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33:261–274. doi:10.1007/s10462-010-9155-0

    Article  Google Scholar 

  4. Bankman IN (2008) Handbook of medical image processing and analysis. Academic press

    Google Scholar 

  5. Beretta L, Gemma M, Anzalone N (2008) The value of MR imaging in posttraumatic diffuse axonal injury. J Emerg Trauma Shock 1(2):126–127

    Article  Google Scholar 

  6. Blake A, Rother C, Brown M, Perez P, Torr PHS (2004) Interactive image segmentation using an adaptive gmmrf model. In: eighth European conference on computer vision, Springer, Prague, pp 428–441

    Google Scholar 

  7. Blatter D, Bigler E, Gale S, Johnson S, Anderson C, Burnett B, Ryser D, Macnamara S, Bailey B (1997) Mr-based brain and cerebrospinal fluid measurement after traumatic brain injury: correlation with neuropsychological outcome. Am J Neuroradiol 18(1):1–10

    Google Scholar 

  8. Bomans M, Hohne KH, Tiede U, Riemer M (1990) 3-D segmentation of MR images of the head for 3-D display. IEEE Trans Med Imaging 9(2):177–183

    Article  Google Scholar 

  9. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient n-d image segmentation. Int J Comput Vision 70:109–131

    Google Scholar 

  10. Boykov Y, Jolly MP (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: proceedings of the eighth IEEE international conference on computer vision, vol 1. pp 105–112

    Google Scholar 

  11. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26:1124–1137

    Article  Google Scholar 

  12. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23:1222–1239

    Article  Google Scholar 

  13. Li C, Goldgof DB, Hall LO (1993) Knowledge-based classification and tissue labeling of MR images of human brain. IEEE Trans Med Imaging 12:740–750

    Google Scholar 

  14. Chang PL, Teng WG (2007) Exploiting the self-organizing map for medical image segmentation. In: 20th IEEE symposium on computer-based medical systems, pp 281–288

    Google Scholar 

  15. Clarke L, Velthuizen R, Camacho M, Heine J, Vaidyanathan M, Hall L, Thatcher R, Silbiger M (1995) MRI segmentation: methods and applications. Magn Reson Imaging 13(3):343–368

    Article  Google Scholar 

  16. Clarke L, Velthuizen R, Phuphanich S, Schellenberg J, Arrington J, Silbiger M (1993) MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 11(1):95–106

    Article  Google Scholar 

  17. Cline H, Lorensen W, Kikinis R, Jolesz F (1990) Three-dimensional segmentation of MR images of the head using probability and connectivity. J Comput Assist Tomogr 14(6):1037–1045

    Article  Google Scholar 

  18. Cowie CJA (2012) Quantitative magnetic resonance imaging in traumatic brain injury. University of Newcastle, Tyne

    Google Scholar 

  19. Dellepiane S (1991) Image segmentation: errors, sensitivity, and uncertainty. In: proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society, vol 13. pp 253–254

    Google Scholar 

  20. Dressler J, Hanisch U, Kuhlisch E, Geiger KD (2007) Neuronal and glial apoptosis in human traumatic brain injury. Int J Legal Med 121(5):365–375

    Article  Google Scholar 

  21. Falcao A, Udupa J, Miyazawa F (2000) An ultra-fast user-steered image segmentation paradigm: live wire on the fly. IEEE Trans Med Imaging 19(1):55–62

    Article  Google Scholar 

  22. Falcao AX, Udupa JK (2000) A 3d generalization of user-steered live-wire segmentation. Med Image Anal 4(4):389–402

    Article  Google Scholar 

  23. Gennarelli TA (1996) The spectrum of traumatic axonal injury. Neuropathol Appl Neurobiol 22(6):509–513

    Article  Google Scholar 

  24. Gennarelli TA, Thibault LE, Adams JH, Graham DI, Thompson CJ, Marcincin RP (1982) Diffuse axonal injury and traumatic coma in the primate. Ann Neurol 12(6):564–574

    Article  Google Scholar 

  25. Gerig G, Martin J, Kikinis R, Kubler O, Shenton M, Jolesz FA (1992) Unsupervised tissue type segmentation of 3d dual-echo MR head data. Image Vis Comput 10:349–360

    Article  Google Scholar 

  26. Hall L, Bensaid A, Clarke L, Velthuizen R, Silbiger M, Bezdek J (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3(5):672–682

    Article  Google Scholar 

  27. Heimann T, Meinzer H-P (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563

    Article  Google Scholar 

  28. Bezdek JC, Hall LO, Clarke LP (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20(4):1033–1048

    Google Scholar 

  29. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331. doi:10.1007/BF00133570

    Article  Google Scholar 

  30. Khotanlou H, Colliot O, Atif J, Bloch I (2009) 3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160:1457–1473

    Article  MathSciNet  Google Scholar 

  31. Kinnunen KM, Greenwood R, Powell JH, Leech R, Hawkins PC, Bonnelle V, Patel MC, Counsell SJ, Sharp DJ (2010) White matter damage and cognitive impairment after traumatic brain injury. Brain 134:449–463

    Google Scholar 

  32. Kumar PM, Torr PHS, Zisserman A (2005) Obj cut. In: CVPR’05: proceedings of the 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE Computer Society, Washington, DC, USA, pp 18–25

    Google Scholar 

  33. Li K, Wu X, Chen DZ, Sonka M (2006) Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE Trans Pattern Anal Mach Intell 28:119–134

    Article  Google Scholar 

  34. Li XY, Feng DF (2009) Diffuse axonal injury: novel insights into detection and treatment. J Clin Neurosci 16(5):614–619

    Article  Google Scholar 

  35. Lundervold A, Storvik G (1995) Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images. IEEE Trans Med Imaging 14:339–349

    Article  Google Scholar 

  36. Brummer ME, Mersereau RM, Eisner RL, Lewine RRJ (1993) Automatic detection of brain contours in MRI data sets. IEEE Trans Med Imaging 12:153–166

    Google Scholar 

  37. Martelli A (1972) Edge detection using heuristic search methods. Comput Graph Image Process 1(2):169–182

    Article  Google Scholar 

  38. Mcdaniel MA (2005) Big-brained people are smarter: a meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence 33(4):337–346

    Article  Google Scholar 

  39. Meythaler JM, Peduzzi JD, Eleftheriou E, Novack TA (2001) Current concepts: diffuse axonal injury[ndash] associated traumatic brain injury. Arch Phys Med Rehabil 82(10):1461–1471

    Article  Google Scholar 

  40. Mitchell JR, Karlik SJ, Lee DH, Fenster A (1994) Computer-assisted identification and quantification of multiple sclerosis lesions in MR imaging volumes in the brain. J Magn Reson Imaging 4(2):197–208

    Article  Google Scholar 

  41. Montanari U (1971) On the optimal detection of curves in noisy pictures. Commun ACM 14:335–345

    Article  MATH  Google Scholar 

  42. Monti E, Pedoia V, Binaghi E, De Benedictis A, Balbi S (2012) Graph based MRI analysis for evaluating the prognostic relevance of longitudinal brain atrophy estimation in post-traumatic diffuse axonal injury. In: proceedings of computatioanl modelling of object presented in images: fondumnentals, methods and application, CompIMAGE, pp 297–302

    Google Scholar 

  43. de Morais DF (2006) Clinical application of magnetic resonance (MR) imaging in injured patients with acute traumatic brain injury. Arq Neuropsiquiatr 64:1051–1051

    Google Scholar 

  44. Ng K, Mikulis DJ, Glazer J, Kabani N, Till C, Greenberg G, Thompson A, Lazinski D, Agid R, Colella B, Green RE (2008) Magnetic resonance imaging evidence of progression of subacute brain atrophy in moderate to severe traumatic brain injury. Arch Phys Med Rehabil 89(12, Suppl):S35–S44. (<>Special issue on traumatic brain injury from the Toronto Rehabilitation Institute TBI recovery study: patterns, predictors, and mechanisms of recovery plus new directions for treatment research<>)

    Google Scholar 

  45. Pannizzo F, Stallmeyer MJB, Friedman J, Jennis RJ, Zabriskie J, Plank C, Zimmerman R, Whalen JP, Cahill PT (1992) Quantitative MRI studies for assessment of multiple sclerosis. Magn Reson Med 24(1):90–99

    Article  Google Scholar 

  46. Pedoia V, Binaghi E (2012) Automatic MRI 2D brain segmentation using graph searching technique. Int J Numer Method Biomed Eng (in press)

    Google Scholar 

  47. Pedoia V, Binaghi E, Balbi S, De Benedictis A, Monti E, Minotto R (2011) 2d MRI brain segmentation by using feasibility constraints. In: proceedings of the vision and medical image processing, VipIMAGE, pp 251–256

    Google Scholar 

  48. Peng Y, Liu R (2010) Object segmentation based on watershed and graph cut. In: 3rd international congress on image and signal processing (CISP), vol 3. pp 1431–1435

    Google Scholar 

  49. Pope DL, Parker DL, Clayton PD, Gustafson DE (1985) Left ventricular border recognition using a dynamic search algorithm. Radiology 155:513–518

    Google Scholar 

  50. Povlishock J, Katz D (2005) Update of neuropathology and neurological recovery after traumatic brain injury. J Head Trauma Rehabil 20(1):76–94

    Google Scholar 

  51. Scheid R, Walther K, Guthke T, Preul C, von Cramon D (2006) Cognitive sequelae of diffuse axonal injury. Arch Neurol 63(3):418–424

    Google Scholar 

  52. Robertson IH (2008) Traumatic brain injury: recovery, prediction, and the clinician. Arch Phys Med Rehabil 89(12, Suppl):S1–S2. (<>Special issue on traumatic brain injury from the Toronto Rehabilitation Institute TBI recovery study: patterns, predictors, and mechanisms of recovery plus new directions for treatment research<>)

    Google Scholar 

  53. Rother C, Kolmogorov V, Blake A (2004) "Grabcut": interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  54. Ho S, Bullitt E, Gerig G (2002) Level set evolution with region competition: automatic 3-d segmentation of brain tumors. Proc Int Conf Pattern Recognit 1:523–535

    Google Scholar 

  55. Shattuck DW, Leahy RM (2002) Brainsuite: an automated cortical surface identification tool. Med Image Anal 6(2):129–142

    Article  Google Scholar 

  56. Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008) Construction of a 3d probabilistic atlas of human cortical structures. NeuroImage 39(3):1064–1080

    Article  Google Scholar 

  57. Shaw NA (2002) The neurophysiology of concussion. Prog Neurobiol 67(4):281–344

    Article  Google Scholar 

  58. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155

    Article  Google Scholar 

  59. Snell JW, Merickel MB, Ortega JM, Goble JC, Brookeman JR, Kassell NF (1994) Segmentation of the brain from 3d mri using a hierarchical active surface template. In: proceedings of the SPIE conference on medical imaging, pp 2–9

    Google Scholar 

  60. Song T, Jamshidi M, Lee R, Huang M (2007) A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Trans Neural Networks 18(5):1424–1432

    Article  Google Scholar 

  61. Sonka M, Hlavac V, Boyle R (1993) Image processing, analysis and machine vision, 3rd edn. Chapman and Hall, London

    Google Scholar 

  62. Sonka M, Winniford M, Collins S (1995a) Robust simultaneous detection of coronary borders in complex images. IEEE Trans Med Imaging 14(1):151–161

    Article  Google Scholar 

  63. Sonka M, Winniford M, Collins S (1995b) Robust simultaneous detection of coronary borders in complex images. IEEE Trans Med Imaging 14(1):151–161

    Article  Google Scholar 

  64. Sonka M, Zhang X, Siebes M, Bissing M, Dejong S, Collins S, McKay C (1995c) Segmentation of intravascular ultrasound images: a knowledge-based approach. IEEE Trans Med Imaging 14(4):719–732

    Article  Google Scholar 

  65. Suzuki H, ichiro Toriwaki J (1991) Automatic segmentation of head MRI images by knowledge guided thresholding. Comput Med Imaging Graph 15(4):233–240. (<>NMR image processing and pattern recognition<>)

    Google Scholar 

  66. Thedens D, Skorton D, Fleagle S (1990) A three-dimensional graph searching technique for cardiac border detection in sequential images and its application to magnetic resonance image data. In: proceedings of computers in cardiology, pp 57–60

    Google Scholar 

  67. Thedens D, Skorton D, Fleagle S (1995) Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging. IEEE Trans Med Imaging 14:42–55

    Article  Google Scholar 

  68. Tian D, Fan L (2007) A brain MR images segmentation method based on som neural network. In: proceedings of the 1st international conference on bioinformatics and biomedical engineering (ICBBE’07), pp 686–689

    Google Scholar 

  69. Wada T, Kuroda K, Yoshida Y, Ogawa A, Endo S (2005) Recovery process of immediate prolonged posttraumatic coma following severe head injury without mass lesions. Neurol Med Chir (Tokyo) 45(12):614–619. (discussion 619–620)

    Google Scholar 

  70. Waks A, Tretiak OJ (1990) Recognition of regions in brain sections. Comput Med Imaging Graph 14(5):341–352. (<>Progress in imaging in the neurosciences using microcomputers and workstations<>)

    Google Scholar 

  71. Warner MA, Marquez De La Plata C (2010) Assessing spatial relationships between axonal integrity, regional brain volumes, and neuropsychological outcomes after traumatic axonal injury. J Neurotrauma 27(12):2121–2130

    Article  Google Scholar 

  72. Cointepas Y, Mangin JF, Garnero L, Poline JB, Benali H (2001) Brain visa: software platform for visualization and analysis of multi-modality brain data. Neuroimage 6:339–349

    Google Scholar 

  73. Zhang DQ, Chen SC (2004) A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 32(1):37–50

    Article  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Biotecnologie e Scienze della Vita, Università degli Studi dell’Insubria, Varese, Italy

    Emanuele Monti & Sergio Balbi

  2. Dipartimento di Scienze Teoriche e Applicate—Sezione Informatica, Università degli Studi dell’Insubria, Varese, Italy

    Valentina Pedoia & Elisabetta Binaghi

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  1. Emanuele Monti
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  2. Valentina Pedoia
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  3. Elisabetta Binaghi
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  4. Sergio Balbi
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Correspondence to Valentina Pedoia .

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Editors and Affiliations

  1. Department of Computer, Control and Management Engineering, Sapienza Università di Roma, Rome, Italy

    Paolo Di Giamberardino

  2. Department of Computer, Control and Management Engineering, Sapienza Università di Roma, Rome, Italy

    Daniela Iacoviello

  3. Department of Mechanical Engineering, Universidade do Porto, Porto, Portugal

    Renato Natal Jorge

  4. Department of mechanical engineering, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Monti, E., Pedoia, V., Binaghi, E., Balbi, S. (2014). Study of the Prognostic Relevance of Longitudinal Brain Atrophy in Post-traumatic Diffuse Axonal Injury Using Graph-Based MRI Segmentation Techniques. In: Di Giamberardino, P., Iacoviello, D., Natal Jorge, R., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Lecture Notes in Computational Vision and Biomechanics, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-04039-4_14

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