Abstract
Cerebral glioma is one of the most aggressive space-occupying diseases, which will exhibit midline shift (MLS) due to mass effect. MLS has been used as an important feature for evaluating the pathological severity and patients’ survival possibility. Automatic quantification of MLS is challenging due to deformation, complex shape and complex grayscale distribution. An automatic method is proposed and validated to estimate MLS in patients with gliomas diagnosed using magnetic resonance imaging (MRI). The deformed midline is approximated by combining mechanical model and local symmetry. An enhanced Voigt model which takes into account the size and spatial information of lesion is devised to predict the deformed midline. A composite local symmetry combining local intensity symmetry and local intensity gradient symmetry is proposed to refine the predicted midline within a local window whose size is determined according to the pinhole camera model. To enhance the MLS accuracy, the axial slice with maximum MSL from each volumetric data has been interpolated from a spatial resolution of 1 mm to 0.33 mm. The proposed method has been validated on 30 publicly available clinical head MRI scans presenting with MLS. It delineates the deformed midline with maximum MLS and yields a mean difference of 0.61 ± 0.27 mm, and average maximum difference of 1.89 ± 1.18 mm from the ground truth. Experiments show that the proposed method will yield better accuracy with the geometric center of pathology being the geometric center of tumor and the pathological region being the whole lesion. It has also been shown that the proposed composite local symmetry achieves significantly higher accuracy than the traditional local intensity symmetry and the local intensity gradient symmetry. To the best of our knowledge, for delineation of deformed midline, this is the first report on both quantification of gliomas and from MRI, which hopefully will provide valuable information for diagnosis and therapy. The study suggests that the size of the whole lesion and the location of tumor (instead of edema or the sum of edema and tumor) are more appropriate to determine the extent of deformation. Composite local symmetry is recommended to represent the local symmetry around the deformed midline. The proposed method could be potentially used to quantify the severity of patients with cerebral gliomas and other brain pathology, as well as to approximate midsagittal surface for brain quantification.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig4_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig5_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig6_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig7_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig8_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig9_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig10_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig11_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig12_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig13_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig14_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13246-015-0372-3/MediaObjects/13246_2015_372_Fig15_HTML.jpg)
Similar content being viewed by others
References
Liao C, **ao I, Wong J (2006) Tracing the deformed midline on brain CT. Biomed Eng-App Bas C 18(6):305–311
Legler JM, Ries LAG, Smith MA, Warren JL, Heineman EF, Kaplan RS, Linet MS (1999) Brain and other central nervous system cancers: recent trends in incidence and mortality. J Natl Cancer I 91(16):1382–1390
Olson JD, Riedel E, DeAngelis LM (2000) Long-term outcome of low-grade oligodendroglioma and mixed glioma. Neurology 54(7):1442–1448
Gamburg ES, Regine WF, Patchell RA, Strottmann JM, Mohiuddin M, Young AB (2000) The prognostic significance of midline shift at presentation on survival in patients with glioblastoma multiforme. Int J Radiat Oncol 48(5):1359–1362
Papadopoulos MC, Saadoun S, Binder DK, Manley GT, Krishna S, Verkman AS (2004) Molecular mechanisms of brain tumor edema. Neuroscience 129(4):1009–1018
Liu R, Li S, Chew L, Boon C, Tchoyosom C, Cheng K, Tian Q, Zhang Z (2009) From hemorrhage to midline shift: a new method of tracing the deformed midline in traumatic brain injury CT images. In: 16th IEEE international conference on image processing, pp 2637–2640
**ao F, Chiang I, Wong J, Tsai Y, Huang K, Liao C (2011) Automatic measurement of midline shift on deformed brains using multiresolution binary level set method and Hough transform. Comput Biol Med 41(9):756–762
**ao F, Liao C, Huang K, Chiang I, Wong J (2010) Automated assessment of midline shift in head injury patients. Clin Neurol Neurosur 112(9):785–790
Liao C, **ao F, Wong J, Chiang I (2009) A multiresolution binary level set method and its application to intracranial hematoma segmentation. Comput Med Imag Grap 33(6):423–430
Chen W, Najarian K, Ward K (2010) Actual midline estimation from brain CT scan using multiple regions shape matching. In: 20th IEEE international conference on pattern recognition, pp 2552–2555
Liu R, Li S, Su B, Tan CL, Leong TY, Pang BC, Lim CCT, Lee CK (2014) Automatic detection and quantification of brain midline shift using anatomical marker model. Comput Med Imag Grap 38(1):1–14
Joseph DD (1990) Fluid dynamics of viscoelastic liquids, vol 84. Springer-Verlag, New York
Miller K, Chinzei K, Orssengo G, Bednarz P (2000) Mechanical properties of brain tissue in vivo: experiment and computer simulation. J Biomech 33(11):1369–1376
Klatt D, Hamhaber U, Asbach P, Braun J, Sack I (2007) Noninvasive assessment of the rheological behavior of human organs using multifrequency MR elastography: a study of brain and liver viscoelasticity. Phys Med Biol 52(24):7281
Sack I, Beierbach B, Wuerfel J, Klatt D, Hamhaber U, PapazoglouS Martus P, Braun J (2009) The impact of aging and gender on brain viscoelasticity. Neuroimage 46(3):652–657
Schiessel H, Metzler R, Blumen A, Nonnenmacher TF (1995) Generalized viscoelastic models: their fractional equations with solutions. J Phys-A-Math Gen 28(23):6567
Zhuang D, Liu Y, Wu J, Yao C, Mao Y, Zhang C, Wang M, Wang W, Zhou L (2011) A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation. Am J Neuroradiol 32(2):395–402
Hu Q, Nowinski WL (2003) A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. Neuroimage 20(4):2154–2166
Meyers MA, Krishan KC (2009) Mechanical behavior of materials. Cambridge University Press, Cambridge
Haslach HW Jr (2005) Nonlinear viscoelastic, thermodynamically consistent, models for biological soft tissue. Biomech Model Mechan 3(3):172–189
Miller K, Chinzei K (2002) Mechanical properties of brain tissue in tension. J Biomech 35(4):483–490
Davidson RJ, Hugdahl K (1996) Brain asymmetry. MIT Press/Bradford Books, Cambridge
Lindeberg T (1993) Scale-space theory in computer vision. Springer, New York
Zitnick CL, Ramnath K (2011) Edge foci interest points. In: IEEE international conference on computer vision, pp 359–366
Lowe DG (1999) Object recognition from local scale-invariant features. In: 7th IEEE international conference on computer vision, pp 1150–1157
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE international conference on computer vision and pattern recognition, pp 886–893
Hauagge DC, Noah S (2012) Image matching using local symmetry features. In: IEEE international conference on computer vision and pattern recognition, pp 206–213
Acknowledgments
This work has been supported by National Program on Key Basic Research Project (Nos. 2013CB733800, 2012CB733803), Key Joint Program of National Natural Science Foundation and Guangdong Province (No. U1201257), and Guandong Innovative Research Team Program (No. 201001D0104648280).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Funding
This study was funded by National Program on Key Basic Research Project (Nos. 2013CB733800, 2012CB733803), Key Joint Program of National Natural Science Foundation and Guangdong Province (No. U1201257), and Guandong Innovative Research Team Program (No. 201001D0104648280).
Informed consent
For this type of retrospective studies, formal consent is not required. Informed consent was obtained from all individual participants included in the study.
Rights and permissions
About this article
Cite this article
Chen, M., Elazab, A., Jia, F. et al. Automatic estimation of midline shift in patients with cerebral glioma based on enhanced voigt model and local symmetry. Australas Phys Eng Sci Med 38, 627–641 (2015). https://doi.org/10.1007/s13246-015-0372-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-015-0372-3